DOMAIN: Health Care
CONTEXT: Computer vision can be used in health care for identifying diseases. In Pneumonia detection we need to detect Inflammation of the lungs. In this challenge, you’re required to build an algorithm to detect a visual signal for pneumonia in medical images. Specifically, your algorithm needs to automatically locate lung opacities on chest radiographs.
DATA DESCRIPTION:
- In the dataset, some of the features are labeled “Not Normal No Lung Opacity”. This extra third class indicates that while pneumonia was determined not to be present, there was nonetheless some type of abnormality on the image and oftentimes this finding may mimic the appearance of true pneumonia. Dicom original images: - Medical images are stored in a special format called DICOM files (*.dcm). They contain a combination of header metadata as well as underlying raw image arrays for pixel data.
- Dataset has been attached along with this project. Please use the same for this capstone project.
- Original link to the dataset : https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/data [ for your reference only ]. You can refer to the details of the dataset in the above link
- Acknowledgements: https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/overview/acknowledgements.
PROJECT OBJECTIVE: Design a DL based algorithm for detecting pneumonia.
Milestone 1
importing important libs
In [1]:
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import math
import keras
from keras.models import Sequential
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report,confusion_matrix
import cv2
import pickle
import csv
import random
from skimage import io
from skimage import measure
from skimage.transform import resize
import tensorflow as tf
from tensorflow import keras
from matplotlib import pyplot as plt
from sklearn.metrics import confusion_matrix,classification_report,accuracy_score
from sklearn import metrics
from sklearn.model_selection import train_test_split
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, Callback
import tensorflow as tf
from tensorflow.keras.layers import Conv2D, Reshape, Concatenate, Flatten, MaxPooling2D, BatchNormalization
from tensorflow.keras.layers import Dense, Dropout, Flatten, Input,GlobalAveragePooling2D
from tensorflow.keras.utils import Sequence
from tensorflow.keras.models import Model, Sequential
from tensorflow.keras import optimizers
from tensorflow.keras.applications.densenet import DenseNet121
from tensorflow.keras.layers import GlobalMaxPooling2D
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.applications.vgg16 import preprocess_input
from tensorflow.keras.applications.vgg19 import VGG19
from tensorflow.keras.applications.vgg19 import preprocess_input
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.applications.resnet50 import preprocess_input
from tensorflow.keras import layers, models
!pip install pydicom
import pydicom as dcm
from pydicom.pixel_data_handlers.util import apply_voi_lut
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/ Requirement already satisfied: pydicom in /usr/local/lib/python3.8/dist-packages (2.3.1)
In [2]:
from google.colab import drive
drive.mount('/content/drive')
Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
Milestone 1¶
Step 1: Import the data. [ 3 points ]
In [ ]:
train_labels = pd.read_csv("drive/My Drive/cv capstone/stage_2_train_labels.csv")
print(train_labels.shape)
print(train_labels.head())
detailed_class_info = pd.read_csv("drive/My Drive/cv capstone/stage_2_detailed_class_info.csv")
print(detailed_class_info.shape)
print(detailed_class_info.head())
(30227, 6)
patientId x y width height Target
0 0004cfab-14fd-4e49-80ba-63a80b6bddd6 NaN NaN NaN NaN 0
1 00313ee0-9eaa-42f4-b0ab-c148ed3241cd NaN NaN NaN NaN 0
2 00322d4d-1c29-4943-afc9-b6754be640eb NaN NaN NaN NaN 0
3 003d8fa0-6bf1-40ed-b54c-ac657f8495c5 NaN NaN NaN NaN 0
4 00436515-870c-4b36-a041-de91049b9ab4 264.0 152.0 213.0 379.0 1
(30227, 2)
patientId class
0 0004cfab-14fd-4e49-80ba-63a80b6bddd6 No Lung Opacity / Not Normal
1 00313ee0-9eaa-42f4-b0ab-c148ed3241cd No Lung Opacity / Not Normal
2 00322d4d-1c29-4943-afc9-b6754be640eb No Lung Opacity / Not Normal
3 003d8fa0-6bf1-40ed-b54c-ac657f8495c5 Normal
4 00436515-870c-4b36-a041-de91049b9ab4 Lung Opacity
In [ ]:
detailed_class_info.isna().sum()
len(detailed_class_info['patientId'].unique())
Out[ ]:
26684
In [ ]:
train_labels.isna().sum()
len(train_labels['patientId'].unique())
Out[ ]:
26684
In [ ]:
print('Total number of null values in bounding boxes columns(x, y, width, height): {}'.format(train_labels[['x', 'y', 'width', 'height']].isnull().sum().to_dict()))
print('Total value counts for the Target column: {}'.format(train_labels['Target'].value_counts().to_dict()))
Total number of null values in bounding boxes columns(x, y, width, height): {'x': 20672, 'y': 20672, 'width': 20672, 'height': 20672}
Total value counts for the Target column: {0: 20672, 1: 9555}
Observation: Total number of null values in bounding boxes columns is equal to total number of 0s in Target column.
In [ ]:
#Analysing the labels dataframe
train_labels.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 30227 entries, 0 to 30226 Data columns (total 6 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 patientId 30227 non-null object 1 x 9555 non-null float64 2 y 9555 non-null float64 3 width 9555 non-null float64 4 height 9555 non-null float64 5 Target 30227 non-null int64 dtypes: float64(4), int64(1), object(1) memory usage: 1.4+ MB
In [ ]:
#Checking for unique patient records ==total number of patients
train_labels.patientId.nunique()
Out[ ]:
26684
In [ ]:
train_labels["patientId"].count()-train_labels.patientId.nunique()
Out[ ]:
3543
Observation: From the above data, we can notice that there are around 30227 rows in stage_2_train_labels.csv but we have a total of 26684 unique patientIDs. Hence 3543 patients have more than one bounding box. Verifying this for a patient with the patientId: 00436515-870c-4b36-a041-de91049b9ab4 is the same from 4th and 5th rows of the dataframe.
In [ ]:
train_labels[train_labels.patientId=='00436515-870c-4b36-a041-de91049b9ab4']
Out[ ]:
| patientId | x | y | width | height | Target | |
|---|---|---|---|---|---|---|
| 4 | 00436515-870c-4b36-a041-de91049b9ab4 | 264.0 | 152.0 | 213.0 | 379.0 | 1 |
| 5 | 00436515-870c-4b36-a041-de91049b9ab4 | 562.0 | 152.0 | 256.0 | 453.0 | 1 |
In [ ]:
sns.countplot(x='Target',data=train_labels);
In [ ]:
detailed_class_info.head()
Out[ ]:
| patientId | class | |
|---|---|---|
| 0 | 0004cfab-14fd-4e49-80ba-63a80b6bddd6 | No Lung Opacity / Not Normal |
| 1 | 00313ee0-9eaa-42f4-b0ab-c148ed3241cd | No Lung Opacity / Not Normal |
| 2 | 00322d4d-1c29-4943-afc9-b6754be640eb | No Lung Opacity / Not Normal |
| 3 | 003d8fa0-6bf1-40ed-b54c-ac657f8495c5 | Normal |
| 4 | 00436515-870c-4b36-a041-de91049b9ab4 | Lung Opacity |
Observation: There are 3 different class values - Normal,Lung Opacity and No Lung Opacity/Not Normal.
In [ ]:
detailed_class_info.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 30227 entries, 0 to 30226 Data columns (total 2 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 patientId 30227 non-null object 1 class 30227 non-null object dtypes: object(2) memory usage: 472.4+ KB
In [ ]:
detailed_class_info.describe()
Out[ ]:
| patientId | class | |
|---|---|---|
| count | 30227 | 30227 |
| unique | 26684 | 3 |
| top | 3239951b-6211-4290-b237-3d9ad17176db | No Lung Opacity / Not Normal |
| freq | 4 | 11821 |
In [ ]:
#Checking for duplicate values
detailed_class_info[detailed_class_info.duplicated()].shape
Out[ ]:
(3543, 2)
Observation: The total number of patients match with the value from training labels which is 26684.
In [ ]:
#Checking the class values distribution
sns.countplot(x='class',data=detailed_class_info);
Observation: From the countplot, it is very much noticeable that all the classes are almost equally distributed. We can further check for duplicate and missing values as well
In [ ]:
#Checking for missing values
detailed_class_info.isnull().sum()
Out[ ]:
patientId 0 class 0 dtype: int64
Observation: There are no missing values in the class info data
Step 2: Map training and testing images to its classes. [ 4 points ]
In [ ]:
# Conctinating the two dataset - 'train_labels' and 'detailed_class_info':
train = pd.concat([train_labels,detailed_class_info['class']], axis = 1)
train.head()
Out[ ]:
| patientId | x | y | width | height | Target | class | |
|---|---|---|---|---|---|---|---|
| 0 | 0004cfab-14fd-4e49-80ba-63a80b6bddd6 | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal |
| 1 | 00313ee0-9eaa-42f4-b0ab-c148ed3241cd | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal |
| 2 | 00322d4d-1c29-4943-afc9-b6754be640eb | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal |
| 3 | 003d8fa0-6bf1-40ed-b54c-ac657f8495c5 | NaN | NaN | NaN | NaN | 0 | Normal |
| 4 | 00436515-870c-4b36-a041-de91049b9ab4 | 264.0 | 152.0 | 213.0 | 379.0 | 1 | Lung Opacity |
In [ ]:
print(train.isna().sum())
print(len(train['patientId'].unique()))
patientId 0 x 20672 y 20672 width 20672 height 20672 Target 0 class 0 dtype: int64 26684
In [ ]:
print(train[train['Target'] == 0].isna().sum())
patientId 0 x 20672 y 20672 width 20672 height 20672 Target 0 class 0 dtype: int64
In [ ]:
print(train[train['class'] == 'Normal'].isna().sum())
print(train[train['class'] == 'No Lung Opacity / Not Normal'].isna().sum())
print(train[train['class'] == 'Lung Opacity'].isna().sum())
patientId 0 x 8851 y 8851 width 8851 height 8851 Target 0 class 0 dtype: int64 patientId 0 x 11821 y 11821 width 11821 height 11821 Target 0 class 0 dtype: int64 patientId 0 x 0 y 0 width 0 height 0 Target 0 class 0 dtype: int64
In [ ]:
train[['x', 'y', 'width', 'height']].isnull().sum().to_dict()
Out[ ]:
{'x': 20672, 'y': 20672, 'width': 20672, 'height': 20672}
Observation: Total number of null values for "Normal" and "No Lung Opacity / Not Normal" is equal to total number of 0s in Target column i.e., who do not have pnemonia have their bounding box columns as NaN
Step 3: Map training and testing images to its annotations [ 4 points ]
In [ ]:
dcm_image_data = dcm.read_file('drive/My Drive/cv capstone/stage_2_train_images/c1edf42b-5958-47ff-a1e7-4f23d99583ba.dcm')
print(dcm_image_data)
Dataset.file_meta ------------------------------- (0002, 0000) File Meta Information Group Length UL: 200 (0002, 0001) File Meta Information Version OB: b'\x00\x01' (0002, 0002) Media Storage SOP Class UID UI: Secondary Capture Image Storage (0002, 0003) Media Storage SOP Instance UID UI: 1.2.276.0.7230010.3.1.4.8323329.22672.1517874443.53337 (0002, 0010) Transfer Syntax UID UI: JPEG Baseline (Process 1) (0002, 0012) Implementation Class UID UI: 1.2.276.0.7230010.3.0.3.6.0 (0002, 0013) Implementation Version Name SH: 'OFFIS_DCMTK_360' ------------------------------------------------- (0008, 0005) Specific Character Set CS: 'ISO_IR 100' (0008, 0016) SOP Class UID UI: Secondary Capture Image Storage (0008, 0018) SOP Instance UID UI: 1.2.276.0.7230010.3.1.4.8323329.22672.1517874443.53337 (0008, 0020) Study Date DA: '19010101' (0008, 0030) Study Time TM: '000000.00' (0008, 0050) Accession Number SH: '' (0008, 0060) Modality CS: 'CR' (0008, 0064) Conversion Type CS: 'WSD' (0008, 0090) Referring Physician's Name PN: '' (0008, 103e) Series Description LO: 'view: PA' (0010, 0010) Patient's Name PN: 'c1edf42b-5958-47ff-a1e7-4f23d99583ba' (0010, 0020) Patient ID LO: 'c1edf42b-5958-47ff-a1e7-4f23d99583ba' (0010, 0030) Patient's Birth Date DA: '' (0010, 0040) Patient's Sex CS: 'F' (0010, 1010) Patient's Age AS: '46' (0018, 0015) Body Part Examined CS: 'CHEST' (0018, 5101) View Position CS: 'PA' (0020, 000d) Study Instance UID UI: 1.2.276.0.7230010.3.1.2.8323329.22672.1517874443.53336 (0020, 000e) Series Instance UID UI: 1.2.276.0.7230010.3.1.3.8323329.22672.1517874443.53335 (0020, 0010) Study ID SH: '' (0020, 0011) Series Number IS: '1' (0020, 0013) Instance Number IS: '1' (0020, 0020) Patient Orientation CS: '' (0028, 0002) Samples per Pixel US: 1 (0028, 0004) Photometric Interpretation CS: 'MONOCHROME2' (0028, 0010) Rows US: 1024 (0028, 0011) Columns US: 1024 (0028, 0030) Pixel Spacing DS: [0.14300000000000002, 0.14300000000000002] (0028, 0100) Bits Allocated US: 8 (0028, 0101) Bits Stored US: 8 (0028, 0102) High Bit US: 7 (0028, 0103) Pixel Representation US: 0 (0028, 2110) Lossy Image Compression CS: '01' (0028, 2114) Lossy Image Compression Method CS: 'ISO_10918_1' (7fe0, 0010) Pixel Data OB: Array of 151784 elements
Observation
- Images are stored in DICOM(Digital Imaging and Communications in Medicine) format with .dcm extension. This DICOM image contains more information like patient age, sex, modality, view position and body part and so on.
- pydicom library is used to rread images
In [ ]:
dcm_image_data = dcm.dcmread('drive/My Drive/cv capstone/stage_2_train_images/c1edf42b-5958-47ff-a1e7-4f23d99583ba.dcm').pixel_array
dcm_image_data .shape
Out[ ]:
(1024, 1024)
Observation: Dimension of images are 1024x1024. It is very large to process so we will reduce it during model training.
In [ ]:
columns = ['Modality', 'Age', 'Sex']
train_path = os.listdir('drive/My Drive/cv capstone/stage_2_train_images/')
class_metadata = train
for col in columns:
class_metadata[col] = None
for index,file_name in enumerate(train_path):
print('processing:',index,file_name)
image_data = dcm.read_file('drive/My Drive/cv capstone/stage_2_train_images/'+file_name)
patient_id = (class_metadata['patientId']==image_data.PatientID)
class_metadata.loc[patient_id,'Modality'] = image_data.Modality
class_metadata.loc[patient_id,'PatientAge'] = pd.to_numeric(image_data.PatientAge)
class_metadata.loc[patient_id,'PatientSex'] = image_data.PatientSex
class_metadata.loc[patient_id,'ViewPosition'] = image_data.ViewPosition
class_metadata.loc[patient_id,'BodyPartExamined'] = image_data.BodyPartExamined
Streaming output truncated to the last 5000 lines.
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In [ ]:
class_metadata.BodyPartExamined.value_counts()
In [ ]:
class_metadata.Modality.value_counts()
Observation:
- "BodyPartExamined" is CHEST which is expected
- "Modality" is CR (Computer Radiography)
In [ ]:
class_metadata.drop(['Age', 'Sex', 'BodyPartExamined'], axis=1,inplace =True)
class_metadata.head()
In [ ]:
picklefile = open('drive/My Drive/cv capstone/train1', 'wb')
pickle.dump(class_metadata, picklefile)
picklefile.close()
In [ ]:
picklefile = open('drive/My Drive/cv capstone/train1', 'rb')
training_data= pickle.load(picklefile)
picklefile.close()
In [ ]:
#Converting age to Numeric as the current data type is a String
training_data['PatientAge'] = training_data.PatientAge.astype(int)
In [ ]:
filepaths=[]
source_dir='drive/My Drive/cv capstone/stage_2_train_images/'
In [ ]:
for f in training_data['patientId']:
fpath=os.path.join(source_dir,f)
filepaths.append(fpath)
In [ ]:
training_data['path']=filepaths
In [ ]:
training_data.sample(5)
Out[ ]:
| patientId | x | y | width | height | Target | class | Modality | PatientAge | PatientSex | ViewPosition | path | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29886 | 28b55977-46e6-4113-a582-e93ba5c25cd6 | NaN | NaN | NaN | NaN | 0 | Normal | CR | 25 | M | PA | drive/My Drive/cv capstone/stage_2_train_image... |
| 19959 | b8043f6e-400d-4bf3-8f4b-4a708c41ffba | NaN | NaN | NaN | NaN | 0 | Normal | CR | 55 | M | PA | drive/My Drive/cv capstone/stage_2_train_image... |
| 28178 | f9a3c734-1483-4385-ab4f-e45853f3f00e | 682.0 | 445.0 | 195.0 | 173.0 | 1 | Lung Opacity | CR | 77 | M | AP | drive/My Drive/cv capstone/stage_2_train_image... |
| 27240 | f218fda3-c484-4836-867d-b7ad037bfe4e | 239.0 | 426.0 | 155.0 | 166.0 | 1 | Lung Opacity | CR | 10 | F | AP | drive/My Drive/cv capstone/stage_2_train_image... |
| 14460 | 8eee2836-b938-4953-b394-99facc3f78e1 | 162.0 | 430.0 | 240.0 | 395.0 | 1 | Lung Opacity | CR | 36 | M | AP | drive/My Drive/cv capstone/stage_2_train_image... |
In [ ]:
import matplotlib.patches as patches
def MapImagesToAnnotations(pid): # mapping images and annotations
data = list(training_data[training_data['patientId']==pid].T.to_dict().values())
for i,dr in enumerate(data):
patientImage =dr['patientId']
target=dr['Target']
cls =dr['class']
f, ax = plt.subplots()
id =dr['path']+'.dcm'
pic = dcm.read_file(id, force =True)
modality = pic.Modality
age = pic.PatientAge
sex = pic.PatientSex
bodyPart = pic.BodyPartExamined
img= pic.pixel_array
ax.imshow(img,cmap=plt.cm.bone)
ax.set_title('ID: {} \nModality: {} Age: {} Sex: {} class: {} Target: {} BodyPartExamined: {}'.format(pid,modality,age,sex,cls,target,bodyPart))
if dr['Target']==1:
x, y, width, height = dr['x'],dr['y'],dr['width'],dr['height']
rect = patches.Rectangle((x, y),width, height,color="blue",alpha = 0.2)
ax.add_patch(rect)
plt.show()
In [ ]:
MapImagesToAnnotations('c1edf42b-5958-47ff-a1e7-4f23d99583ba') #mapping image and annotations
In [ ]:
MapImagesToAnnotations('c1f7889a-9ea9-4acb-b64c-b737c929599a') #mapping image and annotations
Step 4:Preprocessing and Visualisation of different classes [4 Points]
In [ ]:
training_data.describe(include="all").T
Out[ ]:
| count | unique | top | freq | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| patientId | 30227 | 26684 | 3239951b-6211-4290-b237-3d9ad17176db | 4 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| x | 9555.0 | NaN | NaN | NaN | 394.047724 | 204.574172 | 2.0 | 207.0 | 324.0 | 594.0 | 835.0 |
| y | 9555.0 | NaN | NaN | NaN | 366.83956 | 148.940488 | 2.0 | 249.0 | 365.0 | 478.5 | 881.0 |
| width | 9555.0 | NaN | NaN | NaN | 218.471376 | 59.289475 | 40.0 | 177.0 | 217.0 | 259.0 | 528.0 |
| height | 9555.0 | NaN | NaN | NaN | 329.269702 | 157.750755 | 45.0 | 203.0 | 298.0 | 438.0 | 942.0 |
| Target | 30227.0 | NaN | NaN | NaN | 0.316108 | 0.464963 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 |
| class | 30227 | 3 | No Lung Opacity / Not Normal | 11821 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| Modality | 30227 | 1 | CR | 30227 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| PatientAge | 30227.0 | NaN | NaN | NaN | 46.797764 | 16.89294 | 1.0 | 34.0 | 49.0 | 59.0 | 155.0 |
| PatientSex | 30227 | 2 | M | 17216 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| ViewPosition | 30227 | 2 | AP | 15297 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| path | 30227 | 26684 | drive/My Drive/cv capstone/stage_2_train_image... | 4 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
Observation: The mean age is 46 years , where as minimum age is 1 year and the max age is 155 which seems to be an outlier
50% of the patiens are of aroudn 49 age , the std deviation is 16 which sugges that age is not normally distubuted
In [ ]:
training_data.PatientSex.value_counts()
Out[ ]:
M 17216 F 13011 Name: PatientSex, dtype: int64
In [ ]:
## Distbution of Class Among the tragets
fig, ax = plt.subplots(nrows = 1, figsize = (12, 6))
temp = training_data.groupby('Target')['class'].value_counts()
data_target_class = pd.DataFrame(data = {'Values': temp.values}, index = temp.index).reset_index()
sns.barplot(ax = ax, x = 'Target', y = 'Values', hue = 'class', data = data_target_class, palette = 'Set3')
plt.title('class and Target for Chest Exams')
Out[ ]:
Text(0.5, 1.0, 'class and Target for Chest Exams')
In [ ]:
## Distbution of Sex Among the tragets
fig, ax = plt.subplots(nrows = 1, figsize = (12, 6))
temp = training_data.groupby('Target')['PatientSex'].value_counts()
data_target_class = pd.DataFrame(data = {'Values': temp.values}, index = temp.index).reset_index()
sns.barplot(ax = ax, x = 'Target', y = 'Values', hue = 'PatientSex', data = data_target_class, palette = 'Set3')
plt.title('Sex and Target for Chest Exams')
Out[ ]:
Text(0.5, 1.0, 'Sex and Target for Chest Exams')
Observation: The number of males in both category are higher than women
In [ ]:
## Distbution of Sex Among the classes
fig, ax = plt.subplots(nrows = 1, figsize = (12, 6))
temp = training_data.groupby('class')['PatientSex'].value_counts()
data_target_class = pd.DataFrame(data = {'Values': temp.values}, index = temp.index).reset_index()
sns.barplot(ax = ax, x = 'class', y = 'Values', hue = 'PatientSex', data = data_target_class, palette = 'Set3')
plt.title('Sex and class for Chest Exams')
Out[ ]:
Text(0.5, 1.0, 'Sex and class for Chest Exams')
Observation: The number of males in all classes are higher than women
In [ ]:
# plots the distrubution of age
sns.distplot(training_data.PatientAge)
/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning)
Out[ ]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fce42835e20>
Observation: Looks like normal distubution of age
In [ ]:
plt.figure(figsize=(10,5)) # setting the figure size
ax = sns.barplot(x='class', y='PatientAge', data=training_data, palette='muted') # barplot'
Observation: This is the distubution of Age with class, maximum age of person with pneuomina is arund 45
In [ ]:
plt.figure(figsize=(15,7))
sns.boxplot(x='class', y='PatientAge', data= training_data)
plt.show()
Observation: The class which has no pneuomia has few outliers , theie age is somewhere around 155 years so we can clip the upper threshold to 100
In [ ]:
training_data['PatientAge'] = training_data['PatientAge'].clip(training_data['PatientAge'].min(), 100)
In [ ]:
temp = training_data
print('Number of patients in age category ')
temp['CategoryAge'] = pd.cut(temp['PatientAge'], bins = 4, precision = 0, labels = ['<=26', '<=50', '<=75', '<=100'])
temp['CategoryAge'].value_counts()
Number of patients in age category
Out[ ]:
<=75 13318 <=50 12157 <=26 3972 <=100 780 Name: CategoryAge, dtype: int64
In [ ]:
label_count=temp['CategoryAge'].value_counts()
explode = (0.01,0.01,0.01,0.01)
fig1, ax1 = plt.subplots(figsize=(5,5))
ax1.pie(label_count.values, explode=explode, labels=label_count.index, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal')
plt.title('CategoryAge Distribution Overall')
plt.show()
In [ ]:
label_count=temp.loc[temp['Target'] == 1,'CategoryAge'].value_counts()
explode = (0.01,0.01,0.01,0.01)
fig1, ax1 = plt.subplots(figsize=(5,5))
ax1.pie(label_count.values, explode=explode, labels=label_count.index, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal')
plt.title('CategoryAge Distribution with Pneumonia evidence')
plt.show()
Observation:
- Number of patients are highest for age group 51-75 for overall case
- But number of patients are highest for age group 27-50 for Target=1 case.
In [ ]:
training_data['class'].value_counts()
Out[ ]:
No Lung Opacity / Not Normal 11821 Lung Opacity 9555 Normal 8851 Name: class, dtype: int64
Observation:
- There are 8851 normal cases , person with lung opactiry are 9555 and No Lung Opacity / Not Normal are 11821
- Patients with evidence of Pneumonia are associated with Lung Opacity class and target = 1.
- Patients with no definitive effidence of Pneumonia are either of Normal or No Lung Opacity / Not Normal class and target = 0.
In [ ]:
label_count=training_data['class'].value_counts()
explode = (0.01,0.01,0.01)
fig1, ax1 = plt.subplots(figsize=(5,5))
ax1.pie(label_count.values, explode=explode, labels=label_count.index, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal')
plt.title('Class Distribution')
plt.show()
In [ ]:
label_count=training_data['Target'].value_counts()
explode = (0.01,0.01)
fig1, ax1 = plt.subplots(figsize=(5,5))
ax1.pie(label_count.values, explode=explode, labels=label_count.index, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal')
plt.title('Class Distribution')
plt.show()
- Posterior/Anterior (PA): X-ray is taken from back part of chest. So it hits posterior part before anterior part. Patient needs to stand against X-ray machine.
- Anterior/Posterior (AP): X-ray is taken from front part of chest. So it hits anterior part before posterior part. When Patient cannot stand against X-ray machine. this is taken but heart size is exaggerated.
In [ ]:
label_count=training_data['ViewPosition'].value_counts()
explode = (0.01,0.01)
fig1, ax1 = plt.subplots(figsize=(5,5))
ax1.pie(label_count.values, explode=explode, labels=label_count.index, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal')
plt.title('ViewPosition Distribution Overall')
plt.show()
In [ ]:
label_count=training_data.loc[training_data['Target'] == 1,'ViewPosition'].value_counts()
explode = (0.01,0.01)
fig1, ax1 = plt.subplots(figsize=(5,5))
ax1.pie(label_count.values, explode=explode, labels=label_count.index, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal')
plt.title('ViewPosition Distribution with Pneumonia evidence')
plt.show()
Observation:
- ViewPosition = PA and ViewPosition = AP are almost equally distributed in the overall train data
- When Target=1, View position = AP dominates.
In [ ]:
# Distbution of Viewposition Among the tragets
fig, ax = plt.subplots(nrows = 1, figsize = (12, 6))
temp = training_data.groupby('Target')['ViewPosition'].value_counts()
data_target_class = pd.DataFrame(data = {'Values': temp.values}, index = temp.index).reset_index()
sns.barplot(ax = ax, x = 'Target', y = 'Values', hue = 'ViewPosition', data = data_target_class, palette = 'Set3')
plt.title('View Positon and Target for Chest Exams')
Out[ ]:
Text(0.5, 1.0, 'View Positon and Target for Chest Exams')
In [ ]:
# Distbution of Viewposition Among the classes
fig, ax = plt.subplots(nrows = 1, figsize = (12, 6))
temp = training_data.groupby('class')['ViewPosition'].value_counts()
data_target_class = pd.DataFrame(data = {'Values': temp.values}, index = temp.index).reset_index()
sns.barplot(ax = ax, x = 'class', y = 'Values', hue = 'ViewPosition', data = data_target_class, palette = 'Set3')
plt.title('View Positon and class for Chest Exams')
Out[ ]:
Text(0.5, 1.0, 'View Positon and class for Chest Exams')
In [ ]:
from matplotlib.patches import Rectangle
def plot_diagram(train_data, pnemonia_point_color, chest_image_color,text):
fig, ax = plt.subplots(1,1,figsize=(10,10))
plt.title("Pneumonia presence points on the chest image\n{}".format(text))
train_data.plot.scatter(x='xc', y='yc', xlim=(0,1024), ylim=(0,1024), ax=ax, alpha=0.8, marker=".", color=pnemonia_point_color)
for i, train_row in train_data.iterrows():
ax.add_patch(Rectangle(xy=(train_row['x'], train_row['y']),
width=train_row['width'],height=train_row['height'],alpha=3.5e-3, color=chest_image_color))
plt.show()
In [ ]:
temp = training_data
temp['xc'] = temp['x'] + temp['width'] / 2
temp['yc'] = temp['y'] + temp['height'] / 2
rows = (temp['ViewPosition']=='AP') & (temp['Target']==1)
plot_diagram(temp[ rows ],'green', 'yellow', 'View Position: AP')
In [ ]:
rows = (temp['ViewPosition']=='PA') & (temp['Target']==1)
plot_diagram(temp[ rows ],'green', 'yellow', 'View Position: PA')
In [ ]:
training_data.drop(['xc', 'yc','CategoryAge'], axis=1,inplace =True) #dropping unecessary columns now
In [ ]:
bounding_boxes = training_data[training_data['Target'] == 1]
bounding_boxes = bounding_boxes.groupby('patientId').size().to_frame('number_of_bounding_boxes').reset_index()
train_labels = training_data.merge(bounding_boxes, on = 'patientId', how = 'left')
print('Number of patients per bounding boxes in the dataset')
(bounding_boxes.groupby('number_of_bounding_boxes').size().to_frame('number_of_patients_per_bounding_boxes').reset_index().set_index('number_of_bounding_boxes').sort_values(by = 'number_of_bounding_boxes'))
Number of patients per bounding boxes in the dataset
Out[ ]:
| number_of_patients_per_bounding_boxes | |
|---|---|
| number_of_bounding_boxes | |
| 1 | 2614 |
| 2 | 3266 |
| 3 | 119 |
| 4 | 13 |
In [ ]:
train_labels.head()
Out[ ]:
| patientId | x | y | width | height | Target | class | Modality | PatientAge | PatientSex | ViewPosition | path | number_of_bounding_boxes | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0004cfab-14fd-4e49-80ba-63a80b6bddd6 | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal | CR | 51 | F | PA | drive/My Drive/cv capstone/stage_2_train_image... | NaN |
| 1 | 00313ee0-9eaa-42f4-b0ab-c148ed3241cd | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal | CR | 48 | F | PA | drive/My Drive/cv capstone/stage_2_train_image... | NaN |
| 2 | 00322d4d-1c29-4943-afc9-b6754be640eb | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal | CR | 19 | M | AP | drive/My Drive/cv capstone/stage_2_train_image... | NaN |
| 3 | 003d8fa0-6bf1-40ed-b54c-ac657f8495c5 | NaN | NaN | NaN | NaN | 0 | Normal | CR | 28 | M | PA | drive/My Drive/cv capstone/stage_2_train_image... | NaN |
| 4 | 00436515-870c-4b36-a041-de91049b9ab4 | 264.0 | 152.0 | 213.0 | 379.0 | 1 | Lung Opacity | CR | 32 | F | AP | drive/My Drive/cv capstone/stage_2_train_image... | 2.0 |
In [ ]:
train_labels[train_labels['patientId'] == '00436515-870c-4b36-a041-de91049b9ab4']
Out[ ]:
| patientId | x | y | width | height | Target | class | Modality | PatientAge | PatientSex | ViewPosition | path | number_of_bounding_boxes | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4 | 00436515-870c-4b36-a041-de91049b9ab4 | 264.0 | 152.0 | 213.0 | 379.0 | 1 | Lung Opacity | CR | 32 | F | AP | drive/My Drive/cv capstone/stage_2_train_image... | 2.0 |
| 5 | 00436515-870c-4b36-a041-de91049b9ab4 | 562.0 | 152.0 | 256.0 | 453.0 | 1 | Lung Opacity | CR | 32 | F | AP | drive/My Drive/cv capstone/stage_2_train_image... | 2.0 |
Observations: Maximum patients have 2 bounding boxes while 13 patients have 4 bounding boxes.
Disturbution of Classes, there are 39% of patients with No Lung opacity , 29.3% Normal
Step 5: Display images with bounding box. [ 5 points ]
In [ ]:
from matplotlib.patches import Rectangle
def boundbox(data):
image_data = list(data.T.to_dict().values())
f, axes = plt.subplots(2,2, figsize=(16,18))
for index, patient_info in enumerate(image_data):
lungs_image = patient_info['patientId']+'.dcm'
lungs_image_data = dcm.read_file('drive/My Drive/cv capstone/stage_2_train_images/'+lungs_image)
axes[index//2, index%2].imshow(lungs_image_data.pixel_array, cmap=plt.cm.gray)
axes[index//2, index%2].set_title('ID: {}\nModality: {} Age: {} Sex: {} Target: {}\nClass: {}'.format(
patient_info['patientId'],lungs_image_data.Modality, lungs_image_data.PatientAge, lungs_image_data.PatientSex, patient_info['Target'], patient_info['class']))
matched_rows = training_data[training_data['patientId']==patient_info['patientId']]
bounding_box = list(matched_rows.T.to_dict().values())
for j, row in enumerate(bounding_box):
axes[index//2, index%2].add_patch(Rectangle(xy=(row['x'], row['y']),width=row['width'],height=row['height'], color="blue",alpha = 0.1))
plt.show()
X-ray images with ONE bounding box
In [ ]:
data = train_labels[train_labels['number_of_bounding_boxes']==1].sample(4)
boundbox(data)
X-ray images with TWO bounding box
In [ ]:
data = train_labels[train_labels['number_of_bounding_boxes']==2].sample(4)
boundbox(data)
X-ray images with THREE bounding box
In [ ]:
data = train_labels[train_labels['number_of_bounding_boxes']==3].sample(4)
boundbox(data)
X-ray images with FOUR bounding box
In [ ]:
data = train_labels[train_labels['number_of_bounding_boxes']==4].sample(4)
boundbox(data)
X-ray images with TARGET=1
In [ ]:
data = training_data[training_data['Target']==1].sample(4) #target is 1
boundbox(data)
X-ray images with TARGET=0 => no bounding box
In [ ]:
data1 = training_data[training_data['Target']==0].sample(4) #target is 0
boundbox(data1)
Step 6: Design, train and test basic CNN models for classification. [ 10 points]
In [ ]:
training_data.head()
Out[ ]:
| patientId | x | y | width | height | Target | class | Modality | PatientAge | PatientSex | ViewPosition | path | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0004cfab-14fd-4e49-80ba-63a80b6bddd6 | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal | CR | 51 | F | PA | drive/My Drive/cv capstone/stage_2_train_image... |
| 1 | 00313ee0-9eaa-42f4-b0ab-c148ed3241cd | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal | CR | 48 | F | PA | drive/My Drive/cv capstone/stage_2_train_image... |
| 2 | 00322d4d-1c29-4943-afc9-b6754be640eb | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal | CR | 19 | M | AP | drive/My Drive/cv capstone/stage_2_train_image... |
| 3 | 003d8fa0-6bf1-40ed-b54c-ac657f8495c5 | NaN | NaN | NaN | NaN | 0 | Normal | CR | 28 | M | PA | drive/My Drive/cv capstone/stage_2_train_image... |
| 4 | 00436515-870c-4b36-a041-de91049b9ab4 | 264.0 | 152.0 | 213.0 | 379.0 | 1 | Lung Opacity | CR | 32 | F | AP | drive/My Drive/cv capstone/stage_2_train_image... |
In [ ]:
#model_trainigdata = training_data.groupby('Target', group_keys=False).apply(lambda x: x.sample(7000))
model_trainigdata = training_data
In [ ]:
model_trainigdata["Target"].value_counts()
Out[ ]:
0 20672 1 9555 Name: Target, dtype: int64
In [6]:
NEW_IMAGE_SIZE = 128
EPOCH = 20
NUM_CLASSES = 1
NUM_CHANNELS = 3
In [ ]:
from tqdm import tqdm_notebook
def resizeImage(row):
image_data_list = [];
target_list = [];
for index, row in tqdm_notebook(row.iterrows()):
patientId = row.patientId
target = row["Target"]
dcm_file = 'drive/My Drive/cv capstone/stage_2_train_images/' + '{}.dcm'.format(patientId)
image_data_array = dcm.read_file(dcm_file).pixel_array
if len(image_data_array.shape) != 3 or image_data_array.shape[2] != 3:
image_data_array = np.stack((image_data_array,) * 3, -1)
image_array = np.array(image_data_array).astype(np.uint8)
image_data_array = cv2.resize(image_array, (NEW_IMAGE_SIZE, NEW_IMAGE_SIZE), interpolation = cv2.INTER_LINEAR)
image_data_list.append(image_data_array)
target_list.append(target)
return image_data_list, target_list
In [ ]:
imageData, targets = resizeImage(model_trainigdata)
imageData = np.array(imageData)
targets = np.array(targets)
<ipython-input-16-0c5e939fc742>:5: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0 Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook` for index, row in tqdm_notebook(row.iterrows()):
In [ ]:
#create a file
picklefile = open('drive/My Drive/cv capstone/xtrain2', 'wb')
picklefile1 = open('drive/My Drive/cv capstone/y2', 'wb')
#pickle the dataframe
pickle.dump(imageData, picklefile)
pickle.dump(targets, picklefile1)
#close file
picklefile.close()
picklefile1.close()
In [3]:
picklefile = open('drive/My Drive/cv capstone/xtrain2', 'rb')
picklefile1 = open('drive/My Drive/cv capstone/y2', 'rb')
#unpickle the dataframe
imgs= pickle.load(picklefile)
lbl= pickle.load(picklefile1)
#close file
picklefile.close()
picklefile1.close()
#print the dataframe
print(type(imgs))
print(type(lbl))
<class 'numpy.ndarray'> <class 'numpy.ndarray'>
In [4]:
from sklearn.preprocessing import LabelBinarizer
enc = LabelBinarizer()
encoded_targets = enc.fit_transform(lbl)
In [5]:
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(imgs, encoded_targets, test_size=0.3, random_state=50)
X_test, X_val, y_test, y_val = train_test_split(X_test, y_test, test_size=0.5, random_state=50)
In [ ]:
def buildBasicModel(loss='binary_crossentropy', metrics=['accuracy']):
shape_input = (128,128,3)
model = Sequential()
model.add(Conv2D(filters = 32,kernel_size=(5,5),input_shape=shape_input))
model.add(GlobalMaxPooling2D())
model.add(Dense(NUM_CLASSES, activation = "sigmoid"))
model.compile(loss=loss, metrics=metrics)
model.summary()
return model
In [ ]:
model = buildBasicModel()
Model: "sequential_2"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_2 (Conv2D) (None, 124, 124, 32) 2432
global_max_pooling2d_2 (Glo (None, 32) 0
balMaxPooling2D)
dense (Dense) (None, 1) 33
=================================================================
Total params: 2,465
Trainable params: 2,465
Non-trainable params: 0
_________________________________________________________________
In [ ]:
history = model.fit(X_train, y_train, epochs=5, validation_data=(X_val, y_val), batch_size = 32)
Epoch 1/5 662/662 [==============================] - 19s 14ms/step - loss: 4.8446 - accuracy: 0.6142 - val_loss: 2.4039 - val_accuracy: 0.4763 Epoch 2/5 662/662 [==============================] - 9s 13ms/step - loss: 1.1455 - accuracy: 0.6291 - val_loss: 1.1273 - val_accuracy: 0.6944 Epoch 3/5 662/662 [==============================] - 9s 13ms/step - loss: 0.8318 - accuracy: 0.6543 - val_loss: 0.7861 - val_accuracy: 0.6853 Epoch 4/5 662/662 [==============================] - 8s 12ms/step - loss: 0.7590 - accuracy: 0.6679 - val_loss: 0.9675 - val_accuracy: 0.5980 Epoch 5/5 662/662 [==============================] - 8s 12ms/step - loss: 0.7264 - accuracy: 0.6712 - val_loss: 0.6522 - val_accuracy: 0.6950
In [ ]:
loss, accuracy = model.evaluate(X_train, y_train, verbose = 1)
print("Train loss:", loss)
print("Train_Accuracy:", accuracy)
662/662 [==============================] - 4s 6ms/step - loss: 0.6669 - accuracy: 0.6952 Train loss: 0.6669108867645264 Train_Accuracy: 0.6951507925987244
In [ ]:
loss, accuracy = model.evaluate(X_test, y_test, verbose = 1)
print("Test loss:", loss)
print("Test_Accuracy:", accuracy)
142/142 [==============================] - 1s 7ms/step - loss: 0.6785 - accuracy: 0.6861 Test loss: 0.6785261631011963 Test_Accuracy: 0.6861491203308105
In [ ]:
y_pred=(model.predict(X_test) > 0.5).astype("int32")
print('Confusion Matrix:\n',confusion_matrix(y_test,y_pred),'\n')
142/142 [==============================] - 1s 5ms/step Confusion Matrix: [[2901 181] [1242 210]]
In [ ]:
print('Classification Report:\n',classification_report(y_test,y_pred),'\n')
Classification Report:
precision recall f1-score support
0 0.70 0.94 0.80 3082
1 0.54 0.14 0.23 1452
accuracy 0.69 4534
macro avg 0.62 0.54 0.52 4534
weighted avg 0.65 0.69 0.62 4534
In [ ]:
print('Accuracy Score:\n',accuracy_score(y_test,y_pred),'\n')
Accuracy Score: 0.6861490957212175
In [ ]:
eval=model.evaluate(X_test,y_test)
print('Model Evaluation Loss :\n',eval[0])
print('Model Evaluation Accuracy :\n',eval[1])
142/142 [==============================] - 1s 6ms/step - loss: 0.6785 - accuracy: 0.6861 Model Evaluation Loss : 0.6785261631011963 Model Evaluation Accuracy : 0.6861491203308105
In [ ]:
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train loss', 'Validation loss'], loc='best')
plt.show()
#Validation loss is test loss
In [ ]:
from sklearn.metrics import roc_curve
#define metrics
y_pred_keras = model.predict(X_test).ravel()
fpr_keras, tpr_keras, thresholds_keras = roc_curve(y_test, y_pred_keras)
from sklearn.metrics import auc
auc_keras = auc(fpr_keras, tpr_keras)
plt.plot(fpr_keras, tpr_keras, label='Keras (area = {:.3f})'.format(auc_keras))
plt.xlabel('False positive rate')
plt.ylabel('True positive rate')
plt.title('ROC curve')
plt.show()
142/142 [==============================] - 1s 5ms/step
Milestone 2¶
Step 1: Fine tune the trained basic CNN models for classification. [ 5 points ]
In [ ]:
def FineTunedCNNModel(loss='binary_crossentropy', metrics=['accuracy']):
shape_input = (128,128,3)
model = Sequential()
model.add(Conv2D(filters = 32,kernel_size=(5,5), padding = 'Same', activation='relu',input_shape=shape_input))
model.add(Conv2D(filters = 32,kernel_size=(5,5), padding = 'Same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.2))
model.add(Conv2D(filters = 64,kernel_size=(4,4), padding = 'Same', activation='relu'))
model.add(Conv2D(filters = 64,kernel_size=(4,4), padding = 'Same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))
model.add(Dropout(0.3))
model.add(Conv2D(filters = 128,kernel_size=(3,3), padding = 'Same', activation='relu'))
model.add(Conv2D(filters = 128,kernel_size=(3,3), padding = 'Same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))
model.add(Dropout(0.4))
model.add(GlobalMaxPooling2D())
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(NUM_CLASSES, activation = "sigmoid"))
optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
model.summary()
return model
In [ ]:
model = FineTunedCNNModel()
Model: "sequential_3"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_3 (Conv2D) (None, 128, 128, 32) 2432
conv2d_4 (Conv2D) (None, 128, 128, 32) 25632
max_pooling2d (MaxPooling2D (None, 64, 64, 32) 0
)
dropout (Dropout) (None, 64, 64, 32) 0
conv2d_5 (Conv2D) (None, 64, 64, 64) 32832
conv2d_6 (Conv2D) (None, 64, 64, 64) 65600
max_pooling2d_1 (MaxPooling (None, 32, 32, 64) 0
2D)
dropout_1 (Dropout) (None, 32, 32, 64) 0
conv2d_7 (Conv2D) (None, 32, 32, 128) 73856
conv2d_8 (Conv2D) (None, 32, 32, 128) 147584
max_pooling2d_2 (MaxPooling (None, 16, 16, 128) 0
2D)
dropout_2 (Dropout) (None, 16, 16, 128) 0
global_max_pooling2d_3 (Glo (None, 128) 0
balMaxPooling2D)
dense_1 (Dense) (None, 64) 8256
dropout_3 (Dropout) (None, 64) 0
dense_2 (Dense) (None, 1) 65
=================================================================
Total params: 356,257
Trainable params: 356,257
Non-trainable params: 0
_________________________________________________________________
In [ ]:
history = model.fit(X_train, y_train, epochs=EPOCH, validation_data=(X_val, y_val), batch_size = 32)
Epoch 1/20 662/662 [==============================] - 51s 71ms/step - loss: 0.7004 - accuracy: 0.6788 - val_loss: 0.6035 - val_accuracy: 0.6946 Epoch 2/20 662/662 [==============================] - 45s 67ms/step - loss: 0.5772 - accuracy: 0.6895 - val_loss: 0.5682 - val_accuracy: 0.6955 Epoch 3/20 662/662 [==============================] - 44s 66ms/step - loss: 0.5652 - accuracy: 0.6925 - val_loss: 0.6266 - val_accuracy: 0.7045 Epoch 4/20 662/662 [==============================] - 45s 69ms/step - loss: 0.5631 - accuracy: 0.6991 - val_loss: 0.5551 - val_accuracy: 0.7197 Epoch 5/20 662/662 [==============================] - 48s 73ms/step - loss: 0.5551 - accuracy: 0.6984 - val_loss: 0.5652 - val_accuracy: 0.7074 Epoch 6/20 662/662 [==============================] - 45s 67ms/step - loss: 0.5543 - accuracy: 0.6995 - val_loss: 0.5744 - val_accuracy: 0.7178 Epoch 7/20 662/662 [==============================] - 45s 67ms/step - loss: 0.5512 - accuracy: 0.7057 - val_loss: 0.5998 - val_accuracy: 0.6831 Epoch 8/20 662/662 [==============================] - 45s 67ms/step - loss: 0.5484 - accuracy: 0.7100 - val_loss: 0.5757 - val_accuracy: 0.7129 Epoch 9/20 662/662 [==============================] - 44s 67ms/step - loss: 0.5442 - accuracy: 0.7128 - val_loss: 0.5556 - val_accuracy: 0.7001 Epoch 10/20 662/662 [==============================] - 47s 71ms/step - loss: 0.5426 - accuracy: 0.7200 - val_loss: 0.5444 - val_accuracy: 0.7297 Epoch 11/20 662/662 [==============================] - 45s 68ms/step - loss: 0.5382 - accuracy: 0.7217 - val_loss: 0.5408 - val_accuracy: 0.7239 Epoch 12/20 662/662 [==============================] - 44s 67ms/step - loss: 0.5343 - accuracy: 0.7252 - val_loss: 0.5568 - val_accuracy: 0.7246 Epoch 13/20 662/662 [==============================] - 45s 67ms/step - loss: 0.5354 - accuracy: 0.7236 - val_loss: 0.5714 - val_accuracy: 0.7246 Epoch 14/20 662/662 [==============================] - 44s 67ms/step - loss: 0.5346 - accuracy: 0.7255 - val_loss: 0.5746 - val_accuracy: 0.7316 Epoch 15/20 662/662 [==============================] - 45s 68ms/step - loss: 0.5311 - accuracy: 0.7299 - val_loss: 0.5638 - val_accuracy: 0.7142 Epoch 16/20 662/662 [==============================] - 45s 68ms/step - loss: 0.5301 - accuracy: 0.7292 - val_loss: 0.5372 - val_accuracy: 0.7213 Epoch 17/20 662/662 [==============================] - 45s 67ms/step - loss: 0.5136 - accuracy: 0.7490 - val_loss: 0.5358 - val_accuracy: 0.7334 Epoch 18/20 662/662 [==============================] - 45s 68ms/step - loss: 0.5100 - accuracy: 0.7512 - val_loss: 0.4962 - val_accuracy: 0.7588 Epoch 19/20 662/662 [==============================] - 45s 68ms/step - loss: 0.4861 - accuracy: 0.7762 - val_loss: 0.4760 - val_accuracy: 0.7830 Epoch 20/20 662/662 [==============================] - 45s 68ms/step - loss: 0.4734 - accuracy: 0.7791 - val_loss: 0.4922 - val_accuracy: 0.7663
In [ ]:
loss, accuracy = model.evaluate(X_train, y_train, verbose = 1)
print("Train loss:", loss)
print("Train_Accuracy:", accuracy)
662/662 [==============================] - 14s 21ms/step - loss: 0.4843 - accuracy: 0.7759 Train loss: 0.4843025207519531 Train_Accuracy: 0.775924026966095
In [ ]:
loss, accuracy = model.evaluate(X_test, y_test, verbose = 1)
print("Test loss:", loss)
print("Test_Accuracy:", accuracy)
In [ ]:
y_pred=(model.predict(X_test) > 0.5).astype("int32")
print('Confusion Matrix:\n',confusion_matrix(y_test,y_pred),'\n')
142/142 [==============================] - 3s 19ms/step Confusion Matrix: [[2834 248] [ 847 605]]
In [ ]:
print('Classification Report:\n',classification_report(y_test,y_pred),'\n')
Classification Report:
precision recall f1-score support
0 0.77 0.92 0.84 3082
1 0.71 0.42 0.52 1452
accuracy 0.76 4534
macro avg 0.74 0.67 0.68 4534
weighted avg 0.75 0.76 0.74 4534
In [ ]:
print('Accuracy Score:\n',accuracy_score(y_test,y_pred),'\n')
Accuracy Score: 0.7584913983237759
In [ ]:
eval=model.evaluate(X_test,y_test)
print('Model Evaluation Loss :\n',eval[0])
print('Model Evaluation Accuracy :\n',eval[1])
142/142 [==============================] - 3s 22ms/step - loss: 0.5035 - accuracy: 0.7585 Model Evaluation Loss : 0.5034609436988831 Model Evaluation Accuracy : 0.7584913969039917
In [ ]:
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train loss', 'Validation loss'], loc='best')
plt.show()
#Validation loss is test loss
In [ ]:
from sklearn.metrics import roc_curve
#define metrics
y_pred_keras = model.predict(X_test).ravel()
fpr_keras, tpr_keras, thresholds_keras = roc_curve(y_test, y_pred_keras)
from sklearn.metrics import auc
auc_keras = auc(fpr_keras, tpr_keras)
plt.plot(fpr_keras, tpr_keras, label='Keras (area = {:.3f})'.format(auc_keras))
plt.xlabel('False positive rate')
plt.ylabel('True positive rate')
plt.title('ROC curve')
plt.show()
142/142 [==============================] - 3s 19ms/step
Step 2: Apply Transfer Learning model for classification [4 points]
DeneNet with Validation data
In [7]:
import tensorflow as tf
from tensorflow.keras.layers import Dense, Dropout, Flatten, Input,GlobalAveragePooling2D
def build_model():
input_shape = (NEW_IMAGE_SIZE,NEW_IMAGE_SIZE,NUM_CHANNELS)
inputs = Input(shape = input_shape)
initializer = tf.keras.initializers.GlorotNormal()
denseNetModel = DenseNet121(include_top = False, input_tensor = inputs, weights ='imagenet')
densenet = Model(inputs = inputs, outputs = denseNetModel.layers[-1].output, name = 'DenseNet121')
model = Sequential(name = 'DenseNet121')
model.add(densenet)
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.4))
model.add(Dense(1, activation = 'sigmoid', kernel_initializer = initializer))
model.summary()
return model
In [8]:
model = build_model()
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/densenet/densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5
29084464/29084464 [==============================] - 0s 0us/step
Model: "DenseNet121"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
DenseNet121 (Functional) (None, 4, 4, 1024) 7037504
global_average_pooling2d (G (None, 1024) 0
lobalAveragePooling2D)
dropout (Dropout) (None, 1024) 0
dense (Dense) (None, 1) 1025
=================================================================
Total params: 7,038,529
Trainable params: 6,954,881
Non-trainable params: 83,648
_________________________________________________________________
In [9]:
import tensorflow as tf
def cosine_annealing(x):
lr = 0.001
epochs = EPOCH
return lr* (np.cos(np.pi*x/epochs)+1.) /2
learning_rate = tf.keras.callbacks.LearningRateScheduler(cosine_annealing)
early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0.005, patience=4, restore_best_weights=True, verbose=1, mode='auto')
In [10]:
from tensorflow.keras.optimizers import Adam
LEARNING_RATE = 1e-4
LOSS = 'binary_crossentropy'
METRICS = ['accuracy']
model.compile(optimizer = Adam(lr = LEARNING_RATE), loss = LOSS, metrics = METRICS)
/usr/local/lib/python3.8/dist-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead. super(Adam, self).__init__(name, **kwargs)
In [11]:
history=model.fit(X_train,y_train,
validation_data=(X_val,y_val),
callbacks=[learning_rate,early_stopping],
epochs=EPOCH,
batch_size=32)
Epoch 1/20 662/662 [==============================] - 119s 142ms/step - loss: 0.4607 - accuracy: 0.7891 - val_loss: 0.4316 - val_accuracy: 0.8121 - lr: 0.0010 Epoch 2/20 662/662 [==============================] - 88s 132ms/step - loss: 0.4165 - accuracy: 0.8098 - val_loss: 0.9443 - val_accuracy: 0.4192 - lr: 9.9384e-04 Epoch 3/20 662/662 [==============================] - 87s 132ms/step - loss: 0.4015 - accuracy: 0.8166 - val_loss: 0.4087 - val_accuracy: 0.8243 - lr: 9.7553e-04 Epoch 4/20 662/662 [==============================] - 87s 131ms/step - loss: 0.3979 - accuracy: 0.8182 - val_loss: 0.4356 - val_accuracy: 0.7890 - lr: 9.4550e-04 Epoch 5/20 662/662 [==============================] - 87s 131ms/step - loss: 0.3945 - accuracy: 0.8207 - val_loss: 0.4309 - val_accuracy: 0.7916 - lr: 9.0451e-04 Epoch 6/20 662/662 [==============================] - 90s 136ms/step - loss: 0.3769 - accuracy: 0.8267 - val_loss: 0.4237 - val_accuracy: 0.8119 - lr: 8.5355e-04 Epoch 7/20 662/662 [==============================] - ETA: 0s - loss: 0.3632 - accuracy: 0.8375Restoring model weights from the end of the best epoch: 3. 662/662 [==============================] - 88s 133ms/step - loss: 0.3632 - accuracy: 0.8375 - val_loss: 0.4290 - val_accuracy: 0.8183 - lr: 7.9389e-04 Epoch 7: early stopping
In [ ]:
y_pred=(model.predict(X_test) > 0.5).astype("int32")
print('Confusion Matrix:\n',confusion_matrix(y_test,y_pred),'\n')
142/142 [==============================] - 7s 39ms/step Confusion Matrix: [[2751 331] [ 581 871]]
In [ ]:
print('Classification Report:\n',classification_report(y_test,y_pred),'\n')
Classification Report:
precision recall f1-score support
0 0.83 0.89 0.86 3082
1 0.72 0.60 0.66 1452
accuracy 0.80 4534
macro avg 0.78 0.75 0.76 4534
weighted avg 0.79 0.80 0.79 4534
In [ ]:
print('Accuracy Score:\n',accuracy_score(y_test,y_pred),'\n')
Accuracy Score: 0.7988531098367887
In [ ]:
eval=model.evaluate(X_test,y_test,verbose=0)
print('Model Evaluation Loss :\n',eval[0])
print('Model Evaluation Accuracy :\n',eval[1])
Model Evaluation Loss : 0.4331631064414978 Model Evaluation Accuracy : 0.7988530993461609
In [ ]:
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train loss', 'Validation loss'], loc='best')
plt.show()
#Validation loss is test loss
In [12]:
from sklearn.metrics import roc_curve
#define metrics
y_pred_keras = model.predict(X_test).ravel()
fpr_keras, tpr_keras, thresholds_keras = roc_curve(y_test, y_pred_keras)
from sklearn.metrics import auc
auc_keras = auc(fpr_keras, tpr_keras)
plt.plot(fpr_keras, tpr_keras, label='Keras (area = {:.3f})'.format(auc_keras))
plt.xlabel('False positive rate')
plt.ylabel('True positive rate')
plt.title('ROC curve')
plt.show()
142/142 [==============================] - 7s 38ms/step
In [ ]:
plt.imshow(X_test[0], cmap ='gray')
y_pred = model.predict(X_test)
output=(model.predict(X_test) > 0.5).astype("int32")
print("Actual label:", y_test[0])
print("Predicted label:", output[0])
print("Model output:", y_pred[0])
142/142 [==============================] - 5s 33ms/step 142/142 [==============================] - 5s 34ms/step Actual label: [0] Predicted label: [0] Model output: [0.15260364]
No validation data
In [ ]:
X_train, X_test, y_train, y_test = train_test_split(imgs, encoded_targets, test_size=0.2, random_state=50)
In [ ]:
import tensorflow as tf
from tensorflow.keras.layers import Dense, Dropout, Flatten, Input,GlobalAveragePooling2D
def build_DenseNet_model_without_validation_data():
input_shape = (NEW_IMAGE_SIZE,NEW_IMAGE_SIZE,NUM_CHANNELS)
inputs = Input(shape = input_shape)
initializer = tf.keras.initializers.GlorotNormal()
denseNetModel = DenseNet121(include_top = False, input_tensor = inputs, weights ='imagenet')
densenet = Model(inputs = inputs, outputs = denseNetModel.layers[-1].output, name = 'DenseNet121')
model = Sequential(name = 'DenseNet121')
model.add(densenet)
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.4))
model.add(Dense(1, activation = 'sigmoid', kernel_initializer = initializer))
model.summary()
return model
In [ ]:
model_densenet_without_validation_data=build_DenseNet_model_without_validation_data()
Model: "DenseNet121"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
DenseNet121 (Functional) (None, 4, 4, 1024) 7037504
global_average_pooling2d_2 (None, 1024) 0
(GlobalAveragePooling2D)
dropout_6 (Dropout) (None, 1024) 0
dense_4 (Dense) (None, 1) 1025
=================================================================
Total params: 7,038,529
Trainable params: 6,954,881
Non-trainable params: 83,648
_________________________________________________________________
In [ ]:
from tensorflow.keras.optimizers import Adam
LEARNING_RATE = 1e-4
LOSS = 'binary_crossentropy'
METRICS = ['accuracy']
model_densenet_without_validation_data.compile(optimizer = Adam(lr = LEARNING_RATE), loss = LOSS, metrics = METRICS)
/usr/local/lib/python3.8/dist-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead. super(Adam, self).__init__(name, **kwargs)
In [ ]:
history=model_densenet_without_validation_data.fit(X_train,y_train,
validation_data=(X_test,y_test),
epochs=EPOCH,
batch_size=64)
Epoch 1/20 378/378 [==============================] - 119s 276ms/step - loss: 0.5183 - accuracy: 0.7661 - val_loss: 0.4274 - val_accuracy: 0.8205 Epoch 2/20 378/378 [==============================] - 98s 259ms/step - loss: 0.3768 - accuracy: 0.8314 - val_loss: 0.4450 - val_accuracy: 0.8329 Epoch 3/20 378/378 [==============================] - 99s 262ms/step - loss: 0.2981 - accuracy: 0.8656 - val_loss: 0.4005 - val_accuracy: 0.8334 Epoch 4/20 378/378 [==============================] - 98s 260ms/step - loss: 0.2207 - accuracy: 0.9073 - val_loss: 0.5492 - val_accuracy: 0.8133 Epoch 5/20 378/378 [==============================] - 98s 259ms/step - loss: 0.1520 - accuracy: 0.9385 - val_loss: 0.5553 - val_accuracy: 0.7810 Epoch 6/20 378/378 [==============================] - 98s 260ms/step - loss: 0.1129 - accuracy: 0.9562 - val_loss: 0.5555 - val_accuracy: 0.8407 Epoch 7/20 378/378 [==============================] - 98s 258ms/step - loss: 0.0862 - accuracy: 0.9668 - val_loss: 0.6486 - val_accuracy: 0.8402 Epoch 8/20 378/378 [==============================] - 98s 259ms/step - loss: 0.0696 - accuracy: 0.9742 - val_loss: 0.6502 - val_accuracy: 0.8369 Epoch 9/20 378/378 [==============================] - 98s 259ms/step - loss: 0.0577 - accuracy: 0.9799 - val_loss: 0.7761 - val_accuracy: 0.8533 Epoch 10/20 378/378 [==============================] - 98s 259ms/step - loss: 0.0415 - accuracy: 0.9851 - val_loss: 0.6636 - val_accuracy: 0.8407 Epoch 11/20 378/378 [==============================] - 98s 259ms/step - loss: 0.0524 - accuracy: 0.9811 - val_loss: 0.7553 - val_accuracy: 0.8506 Epoch 12/20 378/378 [==============================] - 98s 258ms/step - loss: 0.0368 - accuracy: 0.9866 - val_loss: 0.8412 - val_accuracy: 0.8475 Epoch 13/20 378/378 [==============================] - 98s 259ms/step - loss: 0.0357 - accuracy: 0.9873 - val_loss: 0.7948 - val_accuracy: 0.8480 Epoch 14/20 378/378 [==============================] - 98s 259ms/step - loss: 0.0406 - accuracy: 0.9858 - val_loss: 0.7415 - val_accuracy: 0.8569 Epoch 15/20 378/378 [==============================] - 98s 258ms/step - loss: 0.0327 - accuracy: 0.9881 - val_loss: 0.8761 - val_accuracy: 0.8554 Epoch 16/20 378/378 [==============================] - 98s 259ms/step - loss: 0.0334 - accuracy: 0.9882 - val_loss: 1.0227 - val_accuracy: 0.8559 Epoch 17/20 378/378 [==============================] - 98s 258ms/step - loss: 0.0354 - accuracy: 0.9876 - val_loss: 0.6988 - val_accuracy: 0.8470 Epoch 18/20 378/378 [==============================] - 98s 258ms/step - loss: 0.0312 - accuracy: 0.9884 - val_loss: 1.2778 - val_accuracy: 0.8449 Epoch 19/20 378/378 [==============================] - 98s 259ms/step - loss: 0.0315 - accuracy: 0.9883 - val_loss: 0.9145 - val_accuracy: 0.8311 Epoch 20/20 378/378 [==============================] - 98s 258ms/step - loss: 0.0261 - accuracy: 0.9903 - val_loss: 0.8089 - val_accuracy: 0.8419
In [ ]:
ytrain_pred=(model_densenet_without_validation_data.predict(X_train) > 0.5).astype("int32")
print('Confusion Matrix of train :\n',confusion_matrix(y_train,ytrain_pred),'\n')
756/756 [==============================] - 27s 34ms/step Confusion Matrix of train : [[16222 288] [ 163 7508]]
In [ ]:
y_pred=(model_densenet_without_validation_data.predict(X_test) > 0.5).astype("int32")
print('Confusion Matrix of test:\n',confusion_matrix(y_test,y_pred),'\n')
189/189 [==============================] - 6s 33ms/step Confusion Matrix of test: [[3603 559] [ 397 1487]]
In [ ]:
print('Classification Report of train:\n',classification_report(y_train,ytrain_pred),'\n')
Classification Report of train:
precision recall f1-score support
0 0.99 0.98 0.99 16510
1 0.96 0.98 0.97 7671
accuracy 0.98 24181
macro avg 0.98 0.98 0.98 24181
weighted avg 0.98 0.98 0.98 24181
In [ ]:
print('Classification Report of test:\n',classification_report(y_test,y_pred),'\n')
Classification Report of test:
precision recall f1-score support
0 0.90 0.87 0.88 4162
1 0.73 0.79 0.76 1884
accuracy 0.84 6046
macro avg 0.81 0.83 0.82 6046
weighted avg 0.85 0.84 0.84 6046
In [ ]:
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train loss', 'Validation loss'], loc='best')
plt.show()
#Validation loss is test loss
VGG16
In [13]:
IMAGE_SIZE = [128,128]
vgg = VGG16(input_shape = IMAGE_SIZE + [3], weights = "imagenet", include_top = False)
for layer in vgg.layers:
layer.trainable = False
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5 58889256/58889256 [==============================] - 1s 0us/step
In [14]:
x = Flatten()(vgg.output)
prediction = Dense(1, activation = "sigmoid")(x)
model_vgg16 = Model(inputs = vgg.input, outputs = prediction)
model_vgg16.summary()
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_2 (InputLayer) [(None, 128, 128, 3)] 0
block1_conv1 (Conv2D) (None, 128, 128, 64) 1792
block1_conv2 (Conv2D) (None, 128, 128, 64) 36928
block1_pool (MaxPooling2D) (None, 64, 64, 64) 0
block2_conv1 (Conv2D) (None, 64, 64, 128) 73856
block2_conv2 (Conv2D) (None, 64, 64, 128) 147584
block2_pool (MaxPooling2D) (None, 32, 32, 128) 0
block3_conv1 (Conv2D) (None, 32, 32, 256) 295168
block3_conv2 (Conv2D) (None, 32, 32, 256) 590080
block3_conv3 (Conv2D) (None, 32, 32, 256) 590080
block3_pool (MaxPooling2D) (None, 16, 16, 256) 0
block4_conv1 (Conv2D) (None, 16, 16, 512) 1180160
block4_conv2 (Conv2D) (None, 16, 16, 512) 2359808
block4_conv3 (Conv2D) (None, 16, 16, 512) 2359808
block4_pool (MaxPooling2D) (None, 8, 8, 512) 0
block5_conv1 (Conv2D) (None, 8, 8, 512) 2359808
block5_conv2 (Conv2D) (None, 8, 8, 512) 2359808
block5_conv3 (Conv2D) (None, 8, 8, 512) 2359808
block5_pool (MaxPooling2D) (None, 4, 4, 512) 0
flatten (Flatten) (None, 8192) 0
dense_1 (Dense) (None, 1) 8193
=================================================================
Total params: 14,722,881
Trainable params: 8,193
Non-trainable params: 14,714,688
_________________________________________________________________
In [15]:
model_vgg16.compile(loss = "binary_crossentropy",
optimizer = "adam",
metrics = ["accuracy"])
In [16]:
history=model_vgg16.fit(X_train,y_train,
validation_data=(X_test,y_test),
epochs=EPOCH,
batch_size=32)
Epoch 1/20 662/662 [==============================] - 46s 66ms/step - loss: 2.0466 - accuracy: 0.7402 - val_loss: 1.7433 - val_accuracy: 0.7464 Epoch 2/20 662/662 [==============================] - 41s 62ms/step - loss: 1.4393 - accuracy: 0.7842 - val_loss: 3.2472 - val_accuracy: 0.7177 Epoch 3/20 662/662 [==============================] - 41s 62ms/step - loss: 1.3828 - accuracy: 0.7955 - val_loss: 1.9163 - val_accuracy: 0.7475 Epoch 4/20 662/662 [==============================] - 41s 62ms/step - loss: 1.3962 - accuracy: 0.8082 - val_loss: 1.8397 - val_accuracy: 0.7689 Epoch 5/20 662/662 [==============================] - 41s 62ms/step - loss: 1.1183 - accuracy: 0.8240 - val_loss: 2.0396 - val_accuracy: 0.7552 Epoch 6/20 662/662 [==============================] - 41s 61ms/step - loss: 1.1514 - accuracy: 0.8278 - val_loss: 2.5209 - val_accuracy: 0.7682 Epoch 7/20 662/662 [==============================] - 44s 66ms/step - loss: 1.0045 - accuracy: 0.8404 - val_loss: 2.3588 - val_accuracy: 0.7605 Epoch 8/20 662/662 [==============================] - 41s 61ms/step - loss: 1.0585 - accuracy: 0.8422 - val_loss: 2.1326 - val_accuracy: 0.7726 Epoch 9/20 662/662 [==============================] - 41s 61ms/step - loss: 1.0730 - accuracy: 0.8421 - val_loss: 2.2775 - val_accuracy: 0.7869 Epoch 10/20 662/662 [==============================] - 44s 66ms/step - loss: 1.0268 - accuracy: 0.8497 - val_loss: 2.3570 - val_accuracy: 0.7938 Epoch 11/20 662/662 [==============================] - 41s 62ms/step - loss: 0.9921 - accuracy: 0.8546 - val_loss: 2.8138 - val_accuracy: 0.7446 Epoch 12/20 662/662 [==============================] - 41s 62ms/step - loss: 1.1024 - accuracy: 0.8478 - val_loss: 2.7990 - val_accuracy: 0.7673 Epoch 13/20 662/662 [==============================] - 41s 61ms/step - loss: 1.0333 - accuracy: 0.8562 - val_loss: 2.6145 - val_accuracy: 0.7519 Epoch 14/20 662/662 [==============================] - 41s 61ms/step - loss: 1.0111 - accuracy: 0.8578 - val_loss: 3.2160 - val_accuracy: 0.7252 Epoch 15/20 662/662 [==============================] - 41s 61ms/step - loss: 0.9140 - accuracy: 0.8655 - val_loss: 2.6120 - val_accuracy: 0.7783 Epoch 16/20 662/662 [==============================] - 41s 61ms/step - loss: 0.9519 - accuracy: 0.8653 - val_loss: 3.1988 - val_accuracy: 0.7514 Epoch 17/20 662/662 [==============================] - 40s 61ms/step - loss: 0.9947 - accuracy: 0.8645 - val_loss: 2.7568 - val_accuracy: 0.7777 Epoch 18/20 662/662 [==============================] - 41s 61ms/step - loss: 0.9319 - accuracy: 0.8693 - val_loss: 2.9219 - val_accuracy: 0.7722 Epoch 19/20 662/662 [==============================] - 41s 61ms/step - loss: 0.9722 - accuracy: 0.8645 - val_loss: 3.0654 - val_accuracy: 0.7596 Epoch 20/20 662/662 [==============================] - 40s 61ms/step - loss: 0.8899 - accuracy: 0.8745 - val_loss: 3.0861 - val_accuracy: 0.7728
In [ ]:
ytrain_pred=(model_vgg16.predict(X_train) > 0.5).astype("int32")
print('Confusion Matrix of train :\n',confusion_matrix(y_train,ytrain_pred),'\n')
756/756 [==============================] - 38s 50ms/step Confusion Matrix of train : [[13207 3303] [ 389 7282]]
In [ ]:
y_pred=(model_vgg16.predict(X_test) > 0.5).astype("int32")
print('Confusion Matrix of test:\n',confusion_matrix(y_test,y_pred),'\n')
189/189 [==============================] - 9s 50ms/step Confusion Matrix of test: [[2996 1166] [ 349 1535]]
In [ ]:
print('Classification Report of train:\n',classification_report(y_train,ytrain_pred),'\n')
Classification Report of train:
precision recall f1-score support
0 0.97 0.80 0.88 16510
1 0.69 0.95 0.80 7671
accuracy 0.85 24181
macro avg 0.83 0.87 0.84 24181
weighted avg 0.88 0.85 0.85 24181
In [ ]:
print('Classification Report of test:\n',classification_report(y_test,y_pred),'\n')
Classification Report of test:
precision recall f1-score support
0 0.90 0.72 0.80 4162
1 0.57 0.81 0.67 1884
accuracy 0.75 6046
macro avg 0.73 0.77 0.73 6046
weighted avg 0.79 0.75 0.76 6046
In [ ]:
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train loss', 'Validation loss'], loc='best')
plt.show()
#Validation loss is test loss
In [17]:
from sklearn.metrics import roc_curve
#define metrics
y_pred_keras = model_vgg16.predict(X_test).ravel()
fpr_keras, tpr_keras, thresholds_keras = roc_curve(y_test, y_pred_keras)
from sklearn.metrics import auc
auc_keras = auc(fpr_keras, tpr_keras)
plt.plot(fpr_keras, tpr_keras, label='Keras (area = {:.3f})'.format(auc_keras))
plt.xlabel('False positive rate')
plt.ylabel('True positive rate')
plt.title('ROC curve')
plt.show()
142/142 [==============================] - 7s 48ms/step
VGG19
In [3]:
IMAGE_SIZE = [128,128]
vgg = VGG19(input_shape = IMAGE_SIZE + [3], weights = "imagenet", include_top = False)
for layer in vgg.layers:
layer.trainable = False
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg19/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5 80134624/80134624 [==============================] - 1s 0us/step
In [4]:
x = Flatten()(vgg.output)
prediction = Dense(1, activation = "sigmoid")(x)
model_vgg19 = Model(inputs = vgg.input, outputs = prediction)
model_vgg19.summary()
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 128, 128, 3)] 0
block1_conv1 (Conv2D) (None, 128, 128, 64) 1792
block1_conv2 (Conv2D) (None, 128, 128, 64) 36928
block1_pool (MaxPooling2D) (None, 64, 64, 64) 0
block2_conv1 (Conv2D) (None, 64, 64, 128) 73856
block2_conv2 (Conv2D) (None, 64, 64, 128) 147584
block2_pool (MaxPooling2D) (None, 32, 32, 128) 0
block3_conv1 (Conv2D) (None, 32, 32, 256) 295168
block3_conv2 (Conv2D) (None, 32, 32, 256) 590080
block3_conv3 (Conv2D) (None, 32, 32, 256) 590080
block3_conv4 (Conv2D) (None, 32, 32, 256) 590080
block3_pool (MaxPooling2D) (None, 16, 16, 256) 0
block4_conv1 (Conv2D) (None, 16, 16, 512) 1180160
block4_conv2 (Conv2D) (None, 16, 16, 512) 2359808
block4_conv3 (Conv2D) (None, 16, 16, 512) 2359808
block4_conv4 (Conv2D) (None, 16, 16, 512) 2359808
block4_pool (MaxPooling2D) (None, 8, 8, 512) 0
block5_conv1 (Conv2D) (None, 8, 8, 512) 2359808
block5_conv2 (Conv2D) (None, 8, 8, 512) 2359808
block5_conv3 (Conv2D) (None, 8, 8, 512) 2359808
block5_conv4 (Conv2D) (None, 8, 8, 512) 2359808
block5_pool (MaxPooling2D) (None, 4, 4, 512) 0
flatten (Flatten) (None, 8192) 0
dense (Dense) (None, 1) 8193
=================================================================
Total params: 20,032,577
Trainable params: 8,193
Non-trainable params: 20,024,384
_________________________________________________________________
In [5]:
model_vgg19.compile(loss = "binary_crossentropy",
optimizer = "adam",
metrics = ["accuracy"])
In [18]:
history=model_vgg19.fit(X_train,y_train,
validation_data=(X_test,y_test),
epochs=EPOCH,
batch_size=32)
Epoch 1/20 662/662 [==============================] - 51s 76ms/step - loss: 2.0919 - accuracy: 0.7346 - val_loss: 1.7447 - val_accuracy: 0.7810 Epoch 2/20 662/662 [==============================] - 50s 75ms/step - loss: 1.4544 - accuracy: 0.7767 - val_loss: 1.8662 - val_accuracy: 0.7166 Epoch 3/20 662/662 [==============================] - 49s 73ms/step - loss: 1.2693 - accuracy: 0.7975 - val_loss: 1.7167 - val_accuracy: 0.7644 Epoch 4/20 662/662 [==============================] - 48s 73ms/step - loss: 1.2129 - accuracy: 0.8087 - val_loss: 2.0738 - val_accuracy: 0.7607 Epoch 5/20 662/662 [==============================] - 48s 73ms/step - loss: 1.3124 - accuracy: 0.8109 - val_loss: 2.0432 - val_accuracy: 0.7744 Epoch 6/20 662/662 [==============================] - 48s 73ms/step - loss: 1.2294 - accuracy: 0.8230 - val_loss: 2.1400 - val_accuracy: 0.7728 Epoch 7/20 662/662 [==============================] - 48s 73ms/step - loss: 1.1301 - accuracy: 0.8308 - val_loss: 2.1088 - val_accuracy: 0.7717 Epoch 8/20 662/662 [==============================] - 48s 73ms/step - loss: 1.0224 - accuracy: 0.8400 - val_loss: 2.0817 - val_accuracy: 0.7512 Epoch 9/20 662/662 [==============================] - 48s 73ms/step - loss: 1.0798 - accuracy: 0.8375 - val_loss: 2.6409 - val_accuracy: 0.7594 Epoch 10/20 662/662 [==============================] - 48s 73ms/step - loss: 1.1095 - accuracy: 0.8388 - val_loss: 2.4593 - val_accuracy: 0.7636 Epoch 11/20 662/662 [==============================] - 48s 73ms/step - loss: 0.9547 - accuracy: 0.8499 - val_loss: 2.4799 - val_accuracy: 0.7761 Epoch 12/20 662/662 [==============================] - 48s 73ms/step - loss: 1.0429 - accuracy: 0.8506 - val_loss: 2.4407 - val_accuracy: 0.7616 Epoch 13/20 662/662 [==============================] - 48s 73ms/step - loss: 0.9499 - accuracy: 0.8552 - val_loss: 2.4925 - val_accuracy: 0.7680 Epoch 14/20 662/662 [==============================] - 48s 73ms/step - loss: 1.0337 - accuracy: 0.8539 - val_loss: 2.5048 - val_accuracy: 0.7669 Epoch 15/20 662/662 [==============================] - 48s 73ms/step - loss: 1.0363 - accuracy: 0.8513 - val_loss: 2.9689 - val_accuracy: 0.7596 Epoch 16/20 662/662 [==============================] - 48s 73ms/step - loss: 0.9385 - accuracy: 0.8611 - val_loss: 3.7531 - val_accuracy: 0.7574 Epoch 17/20 662/662 [==============================] - 48s 73ms/step - loss: 0.9629 - accuracy: 0.8605 - val_loss: 2.8424 - val_accuracy: 0.7717 Epoch 18/20 662/662 [==============================] - 48s 73ms/step - loss: 0.9877 - accuracy: 0.8624 - val_loss: 3.3483 - val_accuracy: 0.7671 Epoch 19/20 662/662 [==============================] - 48s 73ms/step - loss: 0.8517 - accuracy: 0.8708 - val_loss: 2.8658 - val_accuracy: 0.7702 Epoch 20/20 662/662 [==============================] - 48s 73ms/step - loss: 0.9228 - accuracy: 0.8681 - val_loss: 2.8610 - val_accuracy: 0.7550
In [ ]:
ytrain_pred=(model_vgg19.predict(X_train) > 0.5).astype("int32")
print('Confusion Matrix of train :\n',confusion_matrix(y_train,ytrain_pred),'\n')
756/756 [==============================] - 45s 59ms/step Confusion Matrix of train : [[15860 650] [ 2390 5281]]
In [ ]:
y_pred=(model_vgg19.predict(X_test) > 0.5).astype("int32")
print('Confusion Matrix of test:\n',confusion_matrix(y_test,y_pred),'\n')
189/189 [==============================] - 11s 57ms/step Confusion Matrix of test: [[3593 569] [ 865 1019]]
In [ ]:
print('Classification Report of train:\n',classification_report(y_train,ytrain_pred),'\n')
Classification Report of train:
precision recall f1-score support
0 0.87 0.96 0.91 16510
1 0.89 0.69 0.78 7671
accuracy 0.87 24181
macro avg 0.88 0.82 0.84 24181
weighted avg 0.88 0.87 0.87 24181
In [ ]:
print('Classification Report of test:\n',classification_report(y_test,y_pred),'\n')
Classification Report of test:
precision recall f1-score support
0 0.81 0.86 0.83 4162
1 0.64 0.54 0.59 1884
accuracy 0.76 6046
macro avg 0.72 0.70 0.71 6046
weighted avg 0.75 0.76 0.76 6046
In [ ]:
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train loss', 'Validation loss'], loc='best')
plt.show()
#Validation loss is test loss
In [21]:
from sklearn.metrics import roc_curve
#define metrics
y_pred_keras = model_vgg19.predict(X_test).ravel()
fpr_keras, tpr_keras, thresholds_keras = roc_curve(y_test, y_pred_keras)
from sklearn.metrics import auc
auc_keras = auc(fpr_keras, tpr_keras)
plt.plot(fpr_keras, tpr_keras, label='Keras (area = {:.3f})'.format(auc_keras))
plt.xlabel('False positive rate')
plt.ylabel('True positive rate')
plt.title('ROC curve')
plt.show()
142/142 [==============================] - 9s 60ms/step
RESNET
In [13]:
IMAGE_SIZE = [128,128]
resnet = ResNet50(input_shape = IMAGE_SIZE + [3], weights = "imagenet", include_top = False)
for layer in resnet.layers:
layer.trainable = False
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5 94765736/94765736 [==============================] - 0s 0us/step
In [14]:
x = Flatten()(resnet.output)
prediction = Dense(1, activation = "sigmoid")(x)
model_resnet = Model(inputs = resnet.input, outputs = prediction)
model_resnet.summary()
Model: "model_1"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_2 (InputLayer) [(None, 128, 128, 3 0 []
)]
conv1_pad (ZeroPadding2D) (None, 134, 134, 3) 0 ['input_2[0][0]']
conv1_conv (Conv2D) (None, 64, 64, 64) 9472 ['conv1_pad[0][0]']
conv1_bn (BatchNormalization) (None, 64, 64, 64) 256 ['conv1_conv[0][0]']
conv1_relu (Activation) (None, 64, 64, 64) 0 ['conv1_bn[0][0]']
pool1_pad (ZeroPadding2D) (None, 66, 66, 64) 0 ['conv1_relu[0][0]']
pool1_pool (MaxPooling2D) (None, 32, 32, 64) 0 ['pool1_pad[0][0]']
conv2_block1_1_conv (Conv2D) (None, 32, 32, 64) 4160 ['pool1_pool[0][0]']
conv2_block1_1_bn (BatchNormal (None, 32, 32, 64) 256 ['conv2_block1_1_conv[0][0]']
ization)
conv2_block1_1_relu (Activatio (None, 32, 32, 64) 0 ['conv2_block1_1_bn[0][0]']
n)
conv2_block1_2_conv (Conv2D) (None, 32, 32, 64) 36928 ['conv2_block1_1_relu[0][0]']
conv2_block1_2_bn (BatchNormal (None, 32, 32, 64) 256 ['conv2_block1_2_conv[0][0]']
ization)
conv2_block1_2_relu (Activatio (None, 32, 32, 64) 0 ['conv2_block1_2_bn[0][0]']
n)
conv2_block1_0_conv (Conv2D) (None, 32, 32, 256) 16640 ['pool1_pool[0][0]']
conv2_block1_3_conv (Conv2D) (None, 32, 32, 256) 16640 ['conv2_block1_2_relu[0][0]']
conv2_block1_0_bn (BatchNormal (None, 32, 32, 256) 1024 ['conv2_block1_0_conv[0][0]']
ization)
conv2_block1_3_bn (BatchNormal (None, 32, 32, 256) 1024 ['conv2_block1_3_conv[0][0]']
ization)
conv2_block1_add (Add) (None, 32, 32, 256) 0 ['conv2_block1_0_bn[0][0]',
'conv2_block1_3_bn[0][0]']
conv2_block1_out (Activation) (None, 32, 32, 256) 0 ['conv2_block1_add[0][0]']
conv2_block2_1_conv (Conv2D) (None, 32, 32, 64) 16448 ['conv2_block1_out[0][0]']
conv2_block2_1_bn (BatchNormal (None, 32, 32, 64) 256 ['conv2_block2_1_conv[0][0]']
ization)
conv2_block2_1_relu (Activatio (None, 32, 32, 64) 0 ['conv2_block2_1_bn[0][0]']
n)
conv2_block2_2_conv (Conv2D) (None, 32, 32, 64) 36928 ['conv2_block2_1_relu[0][0]']
conv2_block2_2_bn (BatchNormal (None, 32, 32, 64) 256 ['conv2_block2_2_conv[0][0]']
ization)
conv2_block2_2_relu (Activatio (None, 32, 32, 64) 0 ['conv2_block2_2_bn[0][0]']
n)
conv2_block2_3_conv (Conv2D) (None, 32, 32, 256) 16640 ['conv2_block2_2_relu[0][0]']
conv2_block2_3_bn (BatchNormal (None, 32, 32, 256) 1024 ['conv2_block2_3_conv[0][0]']
ization)
conv2_block2_add (Add) (None, 32, 32, 256) 0 ['conv2_block1_out[0][0]',
'conv2_block2_3_bn[0][0]']
conv2_block2_out (Activation) (None, 32, 32, 256) 0 ['conv2_block2_add[0][0]']
conv2_block3_1_conv (Conv2D) (None, 32, 32, 64) 16448 ['conv2_block2_out[0][0]']
conv2_block3_1_bn (BatchNormal (None, 32, 32, 64) 256 ['conv2_block3_1_conv[0][0]']
ization)
conv2_block3_1_relu (Activatio (None, 32, 32, 64) 0 ['conv2_block3_1_bn[0][0]']
n)
conv2_block3_2_conv (Conv2D) (None, 32, 32, 64) 36928 ['conv2_block3_1_relu[0][0]']
conv2_block3_2_bn (BatchNormal (None, 32, 32, 64) 256 ['conv2_block3_2_conv[0][0]']
ization)
conv2_block3_2_relu (Activatio (None, 32, 32, 64) 0 ['conv2_block3_2_bn[0][0]']
n)
conv2_block3_3_conv (Conv2D) (None, 32, 32, 256) 16640 ['conv2_block3_2_relu[0][0]']
conv2_block3_3_bn (BatchNormal (None, 32, 32, 256) 1024 ['conv2_block3_3_conv[0][0]']
ization)
conv2_block3_add (Add) (None, 32, 32, 256) 0 ['conv2_block2_out[0][0]',
'conv2_block3_3_bn[0][0]']
conv2_block3_out (Activation) (None, 32, 32, 256) 0 ['conv2_block3_add[0][0]']
conv3_block1_1_conv (Conv2D) (None, 16, 16, 128) 32896 ['conv2_block3_out[0][0]']
conv3_block1_1_bn (BatchNormal (None, 16, 16, 128) 512 ['conv3_block1_1_conv[0][0]']
ization)
conv3_block1_1_relu (Activatio (None, 16, 16, 128) 0 ['conv3_block1_1_bn[0][0]']
n)
conv3_block1_2_conv (Conv2D) (None, 16, 16, 128) 147584 ['conv3_block1_1_relu[0][0]']
conv3_block1_2_bn (BatchNormal (None, 16, 16, 128) 512 ['conv3_block1_2_conv[0][0]']
ization)
conv3_block1_2_relu (Activatio (None, 16, 16, 128) 0 ['conv3_block1_2_bn[0][0]']
n)
conv3_block1_0_conv (Conv2D) (None, 16, 16, 512) 131584 ['conv2_block3_out[0][0]']
conv3_block1_3_conv (Conv2D) (None, 16, 16, 512) 66048 ['conv3_block1_2_relu[0][0]']
conv3_block1_0_bn (BatchNormal (None, 16, 16, 512) 2048 ['conv3_block1_0_conv[0][0]']
ization)
conv3_block1_3_bn (BatchNormal (None, 16, 16, 512) 2048 ['conv3_block1_3_conv[0][0]']
ization)
conv3_block1_add (Add) (None, 16, 16, 512) 0 ['conv3_block1_0_bn[0][0]',
'conv3_block1_3_bn[0][0]']
conv3_block1_out (Activation) (None, 16, 16, 512) 0 ['conv3_block1_add[0][0]']
conv3_block2_1_conv (Conv2D) (None, 16, 16, 128) 65664 ['conv3_block1_out[0][0]']
conv3_block2_1_bn (BatchNormal (None, 16, 16, 128) 512 ['conv3_block2_1_conv[0][0]']
ization)
conv3_block2_1_relu (Activatio (None, 16, 16, 128) 0 ['conv3_block2_1_bn[0][0]']
n)
conv3_block2_2_conv (Conv2D) (None, 16, 16, 128) 147584 ['conv3_block2_1_relu[0][0]']
conv3_block2_2_bn (BatchNormal (None, 16, 16, 128) 512 ['conv3_block2_2_conv[0][0]']
ization)
conv3_block2_2_relu (Activatio (None, 16, 16, 128) 0 ['conv3_block2_2_bn[0][0]']
n)
conv3_block2_3_conv (Conv2D) (None, 16, 16, 512) 66048 ['conv3_block2_2_relu[0][0]']
conv3_block2_3_bn (BatchNormal (None, 16, 16, 512) 2048 ['conv3_block2_3_conv[0][0]']
ization)
conv3_block2_add (Add) (None, 16, 16, 512) 0 ['conv3_block1_out[0][0]',
'conv3_block2_3_bn[0][0]']
conv3_block2_out (Activation) (None, 16, 16, 512) 0 ['conv3_block2_add[0][0]']
conv3_block3_1_conv (Conv2D) (None, 16, 16, 128) 65664 ['conv3_block2_out[0][0]']
conv3_block3_1_bn (BatchNormal (None, 16, 16, 128) 512 ['conv3_block3_1_conv[0][0]']
ization)
conv3_block3_1_relu (Activatio (None, 16, 16, 128) 0 ['conv3_block3_1_bn[0][0]']
n)
conv3_block3_2_conv (Conv2D) (None, 16, 16, 128) 147584 ['conv3_block3_1_relu[0][0]']
conv3_block3_2_bn (BatchNormal (None, 16, 16, 128) 512 ['conv3_block3_2_conv[0][0]']
ization)
conv3_block3_2_relu (Activatio (None, 16, 16, 128) 0 ['conv3_block3_2_bn[0][0]']
n)
conv3_block3_3_conv (Conv2D) (None, 16, 16, 512) 66048 ['conv3_block3_2_relu[0][0]']
conv3_block3_3_bn (BatchNormal (None, 16, 16, 512) 2048 ['conv3_block3_3_conv[0][0]']
ization)
conv3_block3_add (Add) (None, 16, 16, 512) 0 ['conv3_block2_out[0][0]',
'conv3_block3_3_bn[0][0]']
conv3_block3_out (Activation) (None, 16, 16, 512) 0 ['conv3_block3_add[0][0]']
conv3_block4_1_conv (Conv2D) (None, 16, 16, 128) 65664 ['conv3_block3_out[0][0]']
conv3_block4_1_bn (BatchNormal (None, 16, 16, 128) 512 ['conv3_block4_1_conv[0][0]']
ization)
conv3_block4_1_relu (Activatio (None, 16, 16, 128) 0 ['conv3_block4_1_bn[0][0]']
n)
conv3_block4_2_conv (Conv2D) (None, 16, 16, 128) 147584 ['conv3_block4_1_relu[0][0]']
conv3_block4_2_bn (BatchNormal (None, 16, 16, 128) 512 ['conv3_block4_2_conv[0][0]']
ization)
conv3_block4_2_relu (Activatio (None, 16, 16, 128) 0 ['conv3_block4_2_bn[0][0]']
n)
conv3_block4_3_conv (Conv2D) (None, 16, 16, 512) 66048 ['conv3_block4_2_relu[0][0]']
conv3_block4_3_bn (BatchNormal (None, 16, 16, 512) 2048 ['conv3_block4_3_conv[0][0]']
ization)
conv3_block4_add (Add) (None, 16, 16, 512) 0 ['conv3_block3_out[0][0]',
'conv3_block4_3_bn[0][0]']
conv3_block4_out (Activation) (None, 16, 16, 512) 0 ['conv3_block4_add[0][0]']
conv4_block1_1_conv (Conv2D) (None, 8, 8, 256) 131328 ['conv3_block4_out[0][0]']
conv4_block1_1_bn (BatchNormal (None, 8, 8, 256) 1024 ['conv4_block1_1_conv[0][0]']
ization)
conv4_block1_1_relu (Activatio (None, 8, 8, 256) 0 ['conv4_block1_1_bn[0][0]']
n)
conv4_block1_2_conv (Conv2D) (None, 8, 8, 256) 590080 ['conv4_block1_1_relu[0][0]']
conv4_block1_2_bn (BatchNormal (None, 8, 8, 256) 1024 ['conv4_block1_2_conv[0][0]']
ization)
conv4_block1_2_relu (Activatio (None, 8, 8, 256) 0 ['conv4_block1_2_bn[0][0]']
n)
conv4_block1_0_conv (Conv2D) (None, 8, 8, 1024) 525312 ['conv3_block4_out[0][0]']
conv4_block1_3_conv (Conv2D) (None, 8, 8, 1024) 263168 ['conv4_block1_2_relu[0][0]']
conv4_block1_0_bn (BatchNormal (None, 8, 8, 1024) 4096 ['conv4_block1_0_conv[0][0]']
ization)
conv4_block1_3_bn (BatchNormal (None, 8, 8, 1024) 4096 ['conv4_block1_3_conv[0][0]']
ization)
conv4_block1_add (Add) (None, 8, 8, 1024) 0 ['conv4_block1_0_bn[0][0]',
'conv4_block1_3_bn[0][0]']
conv4_block1_out (Activation) (None, 8, 8, 1024) 0 ['conv4_block1_add[0][0]']
conv4_block2_1_conv (Conv2D) (None, 8, 8, 256) 262400 ['conv4_block1_out[0][0]']
conv4_block2_1_bn (BatchNormal (None, 8, 8, 256) 1024 ['conv4_block2_1_conv[0][0]']
ization)
conv4_block2_1_relu (Activatio (None, 8, 8, 256) 0 ['conv4_block2_1_bn[0][0]']
n)
conv4_block2_2_conv (Conv2D) (None, 8, 8, 256) 590080 ['conv4_block2_1_relu[0][0]']
conv4_block2_2_bn (BatchNormal (None, 8, 8, 256) 1024 ['conv4_block2_2_conv[0][0]']
ization)
conv4_block2_2_relu (Activatio (None, 8, 8, 256) 0 ['conv4_block2_2_bn[0][0]']
n)
conv4_block2_3_conv (Conv2D) (None, 8, 8, 1024) 263168 ['conv4_block2_2_relu[0][0]']
conv4_block2_3_bn (BatchNormal (None, 8, 8, 1024) 4096 ['conv4_block2_3_conv[0][0]']
ization)
conv4_block2_add (Add) (None, 8, 8, 1024) 0 ['conv4_block1_out[0][0]',
'conv4_block2_3_bn[0][0]']
conv4_block2_out (Activation) (None, 8, 8, 1024) 0 ['conv4_block2_add[0][0]']
conv4_block3_1_conv (Conv2D) (None, 8, 8, 256) 262400 ['conv4_block2_out[0][0]']
conv4_block3_1_bn (BatchNormal (None, 8, 8, 256) 1024 ['conv4_block3_1_conv[0][0]']
ization)
conv4_block3_1_relu (Activatio (None, 8, 8, 256) 0 ['conv4_block3_1_bn[0][0]']
n)
conv4_block3_2_conv (Conv2D) (None, 8, 8, 256) 590080 ['conv4_block3_1_relu[0][0]']
conv4_block3_2_bn (BatchNormal (None, 8, 8, 256) 1024 ['conv4_block3_2_conv[0][0]']
ization)
conv4_block3_2_relu (Activatio (None, 8, 8, 256) 0 ['conv4_block3_2_bn[0][0]']
n)
conv4_block3_3_conv (Conv2D) (None, 8, 8, 1024) 263168 ['conv4_block3_2_relu[0][0]']
conv4_block3_3_bn (BatchNormal (None, 8, 8, 1024) 4096 ['conv4_block3_3_conv[0][0]']
ization)
conv4_block3_add (Add) (None, 8, 8, 1024) 0 ['conv4_block2_out[0][0]',
'conv4_block3_3_bn[0][0]']
conv4_block3_out (Activation) (None, 8, 8, 1024) 0 ['conv4_block3_add[0][0]']
conv4_block4_1_conv (Conv2D) (None, 8, 8, 256) 262400 ['conv4_block3_out[0][0]']
conv4_block4_1_bn (BatchNormal (None, 8, 8, 256) 1024 ['conv4_block4_1_conv[0][0]']
ization)
conv4_block4_1_relu (Activatio (None, 8, 8, 256) 0 ['conv4_block4_1_bn[0][0]']
n)
conv4_block4_2_conv (Conv2D) (None, 8, 8, 256) 590080 ['conv4_block4_1_relu[0][0]']
conv4_block4_2_bn (BatchNormal (None, 8, 8, 256) 1024 ['conv4_block4_2_conv[0][0]']
ization)
conv4_block4_2_relu (Activatio (None, 8, 8, 256) 0 ['conv4_block4_2_bn[0][0]']
n)
conv4_block4_3_conv (Conv2D) (None, 8, 8, 1024) 263168 ['conv4_block4_2_relu[0][0]']
conv4_block4_3_bn (BatchNormal (None, 8, 8, 1024) 4096 ['conv4_block4_3_conv[0][0]']
ization)
conv4_block4_add (Add) (None, 8, 8, 1024) 0 ['conv4_block3_out[0][0]',
'conv4_block4_3_bn[0][0]']
conv4_block4_out (Activation) (None, 8, 8, 1024) 0 ['conv4_block4_add[0][0]']
conv4_block5_1_conv (Conv2D) (None, 8, 8, 256) 262400 ['conv4_block4_out[0][0]']
conv4_block5_1_bn (BatchNormal (None, 8, 8, 256) 1024 ['conv4_block5_1_conv[0][0]']
ization)
conv4_block5_1_relu (Activatio (None, 8, 8, 256) 0 ['conv4_block5_1_bn[0][0]']
n)
conv4_block5_2_conv (Conv2D) (None, 8, 8, 256) 590080 ['conv4_block5_1_relu[0][0]']
conv4_block5_2_bn (BatchNormal (None, 8, 8, 256) 1024 ['conv4_block5_2_conv[0][0]']
ization)
conv4_block5_2_relu (Activatio (None, 8, 8, 256) 0 ['conv4_block5_2_bn[0][0]']
n)
conv4_block5_3_conv (Conv2D) (None, 8, 8, 1024) 263168 ['conv4_block5_2_relu[0][0]']
conv4_block5_3_bn (BatchNormal (None, 8, 8, 1024) 4096 ['conv4_block5_3_conv[0][0]']
ization)
conv4_block5_add (Add) (None, 8, 8, 1024) 0 ['conv4_block4_out[0][0]',
'conv4_block5_3_bn[0][0]']
conv4_block5_out (Activation) (None, 8, 8, 1024) 0 ['conv4_block5_add[0][0]']
conv4_block6_1_conv (Conv2D) (None, 8, 8, 256) 262400 ['conv4_block5_out[0][0]']
conv4_block6_1_bn (BatchNormal (None, 8, 8, 256) 1024 ['conv4_block6_1_conv[0][0]']
ization)
conv4_block6_1_relu (Activatio (None, 8, 8, 256) 0 ['conv4_block6_1_bn[0][0]']
n)
conv4_block6_2_conv (Conv2D) (None, 8, 8, 256) 590080 ['conv4_block6_1_relu[0][0]']
conv4_block6_2_bn (BatchNormal (None, 8, 8, 256) 1024 ['conv4_block6_2_conv[0][0]']
ization)
conv4_block6_2_relu (Activatio (None, 8, 8, 256) 0 ['conv4_block6_2_bn[0][0]']
n)
conv4_block6_3_conv (Conv2D) (None, 8, 8, 1024) 263168 ['conv4_block6_2_relu[0][0]']
conv4_block6_3_bn (BatchNormal (None, 8, 8, 1024) 4096 ['conv4_block6_3_conv[0][0]']
ization)
conv4_block6_add (Add) (None, 8, 8, 1024) 0 ['conv4_block5_out[0][0]',
'conv4_block6_3_bn[0][0]']
conv4_block6_out (Activation) (None, 8, 8, 1024) 0 ['conv4_block6_add[0][0]']
conv5_block1_1_conv (Conv2D) (None, 4, 4, 512) 524800 ['conv4_block6_out[0][0]']
conv5_block1_1_bn (BatchNormal (None, 4, 4, 512) 2048 ['conv5_block1_1_conv[0][0]']
ization)
conv5_block1_1_relu (Activatio (None, 4, 4, 512) 0 ['conv5_block1_1_bn[0][0]']
n)
conv5_block1_2_conv (Conv2D) (None, 4, 4, 512) 2359808 ['conv5_block1_1_relu[0][0]']
conv5_block1_2_bn (BatchNormal (None, 4, 4, 512) 2048 ['conv5_block1_2_conv[0][0]']
ization)
conv5_block1_2_relu (Activatio (None, 4, 4, 512) 0 ['conv5_block1_2_bn[0][0]']
n)
conv5_block1_0_conv (Conv2D) (None, 4, 4, 2048) 2099200 ['conv4_block6_out[0][0]']
conv5_block1_3_conv (Conv2D) (None, 4, 4, 2048) 1050624 ['conv5_block1_2_relu[0][0]']
conv5_block1_0_bn (BatchNormal (None, 4, 4, 2048) 8192 ['conv5_block1_0_conv[0][0]']
ization)
conv5_block1_3_bn (BatchNormal (None, 4, 4, 2048) 8192 ['conv5_block1_3_conv[0][0]']
ization)
conv5_block1_add (Add) (None, 4, 4, 2048) 0 ['conv5_block1_0_bn[0][0]',
'conv5_block1_3_bn[0][0]']
conv5_block1_out (Activation) (None, 4, 4, 2048) 0 ['conv5_block1_add[0][0]']
conv5_block2_1_conv (Conv2D) (None, 4, 4, 512) 1049088 ['conv5_block1_out[0][0]']
conv5_block2_1_bn (BatchNormal (None, 4, 4, 512) 2048 ['conv5_block2_1_conv[0][0]']
ization)
conv5_block2_1_relu (Activatio (None, 4, 4, 512) 0 ['conv5_block2_1_bn[0][0]']
n)
conv5_block2_2_conv (Conv2D) (None, 4, 4, 512) 2359808 ['conv5_block2_1_relu[0][0]']
conv5_block2_2_bn (BatchNormal (None, 4, 4, 512) 2048 ['conv5_block2_2_conv[0][0]']
ization)
conv5_block2_2_relu (Activatio (None, 4, 4, 512) 0 ['conv5_block2_2_bn[0][0]']
n)
conv5_block2_3_conv (Conv2D) (None, 4, 4, 2048) 1050624 ['conv5_block2_2_relu[0][0]']
conv5_block2_3_bn (BatchNormal (None, 4, 4, 2048) 8192 ['conv5_block2_3_conv[0][0]']
ization)
conv5_block2_add (Add) (None, 4, 4, 2048) 0 ['conv5_block1_out[0][0]',
'conv5_block2_3_bn[0][0]']
conv5_block2_out (Activation) (None, 4, 4, 2048) 0 ['conv5_block2_add[0][0]']
conv5_block3_1_conv (Conv2D) (None, 4, 4, 512) 1049088 ['conv5_block2_out[0][0]']
conv5_block3_1_bn (BatchNormal (None, 4, 4, 512) 2048 ['conv5_block3_1_conv[0][0]']
ization)
conv5_block3_1_relu (Activatio (None, 4, 4, 512) 0 ['conv5_block3_1_bn[0][0]']
n)
conv5_block3_2_conv (Conv2D) (None, 4, 4, 512) 2359808 ['conv5_block3_1_relu[0][0]']
conv5_block3_2_bn (BatchNormal (None, 4, 4, 512) 2048 ['conv5_block3_2_conv[0][0]']
ization)
conv5_block3_2_relu (Activatio (None, 4, 4, 512) 0 ['conv5_block3_2_bn[0][0]']
n)
conv5_block3_3_conv (Conv2D) (None, 4, 4, 2048) 1050624 ['conv5_block3_2_relu[0][0]']
conv5_block3_3_bn (BatchNormal (None, 4, 4, 2048) 8192 ['conv5_block3_3_conv[0][0]']
ization)
conv5_block3_add (Add) (None, 4, 4, 2048) 0 ['conv5_block2_out[0][0]',
'conv5_block3_3_bn[0][0]']
conv5_block3_out (Activation) (None, 4, 4, 2048) 0 ['conv5_block3_add[0][0]']
flatten_1 (Flatten) (None, 32768) 0 ['conv5_block3_out[0][0]']
dense_1 (Dense) (None, 1) 32769 ['flatten_1[0][0]']
==================================================================================================
Total params: 23,620,481
Trainable params: 32,769
Non-trainable params: 23,587,712
__________________________________________________________________________________________________
In [15]:
model_resnet.compile(loss = "binary_crossentropy",
optimizer = "adam",
metrics = ["accuracy"])
In [19]:
history=model_resnet.fit(X_train,y_train,
validation_data=(X_test,y_test),
epochs=EPOCH,
batch_size=32)
Epoch 1/20 662/662 [==============================] - 35s 48ms/step - loss: 1.0551 - accuracy: 0.7523 - val_loss: 0.9840 - val_accuracy: 0.7611 Epoch 2/20 662/662 [==============================] - 30s 45ms/step - loss: 0.6879 - accuracy: 0.8182 - val_loss: 0.9882 - val_accuracy: 0.7750 Epoch 3/20 662/662 [==============================] - 30s 45ms/step - loss: 0.5167 - accuracy: 0.8547 - val_loss: 1.3641 - val_accuracy: 0.7558 Epoch 4/20 662/662 [==============================] - 30s 46ms/step - loss: 0.3814 - accuracy: 0.8860 - val_loss: 1.0551 - val_accuracy: 0.7746 Epoch 5/20 662/662 [==============================] - 30s 45ms/step - loss: 0.3390 - accuracy: 0.8981 - val_loss: 1.1372 - val_accuracy: 0.7812 Epoch 6/20 662/662 [==============================] - 30s 45ms/step - loss: 0.2922 - accuracy: 0.9142 - val_loss: 1.1592 - val_accuracy: 0.8068 Epoch 7/20 662/662 [==============================] - 30s 45ms/step - loss: 0.3072 - accuracy: 0.9163 - val_loss: 1.2740 - val_accuracy: 0.7900 Epoch 8/20 662/662 [==============================] - 30s 45ms/step - loss: 0.2288 - accuracy: 0.9296 - val_loss: 2.0476 - val_accuracy: 0.7655 Epoch 9/20 662/662 [==============================] - 30s 45ms/step - loss: 0.2103 - accuracy: 0.9365 - val_loss: 1.5370 - val_accuracy: 0.7638 Epoch 10/20 662/662 [==============================] - 30s 45ms/step - loss: 0.2163 - accuracy: 0.9381 - val_loss: 1.6757 - val_accuracy: 0.7662 Epoch 11/20 662/662 [==============================] - 30s 46ms/step - loss: 0.2138 - accuracy: 0.9397 - val_loss: 1.5527 - val_accuracy: 0.7931 Epoch 12/20 662/662 [==============================] - 30s 45ms/step - loss: 0.1766 - accuracy: 0.9477 - val_loss: 1.6334 - val_accuracy: 0.8017 Epoch 13/20 662/662 [==============================] - 30s 45ms/step - loss: 0.1146 - accuracy: 0.9645 - val_loss: 1.4953 - val_accuracy: 0.8090 Epoch 14/20 662/662 [==============================] - 31s 46ms/step - loss: 0.1380 - accuracy: 0.9594 - val_loss: 1.7242 - val_accuracy: 0.8167 Epoch 15/20 662/662 [==============================] - 30s 46ms/step - loss: 0.1961 - accuracy: 0.9497 - val_loss: 1.7273 - val_accuracy: 0.8030 Epoch 16/20 662/662 [==============================] - 30s 46ms/step - loss: 0.1333 - accuracy: 0.9620 - val_loss: 1.9328 - val_accuracy: 0.8114 Epoch 17/20 662/662 [==============================] - 30s 46ms/step - loss: 0.1209 - accuracy: 0.9650 - val_loss: 1.8536 - val_accuracy: 0.8121 Epoch 18/20 662/662 [==============================] - 30s 45ms/step - loss: 0.1738 - accuracy: 0.9608 - val_loss: 1.9569 - val_accuracy: 0.8244 Epoch 19/20 662/662 [==============================] - 30s 45ms/step - loss: 0.0697 - accuracy: 0.9780 - val_loss: 1.9347 - val_accuracy: 0.8176 Epoch 20/20 662/662 [==============================] - 30s 45ms/step - loss: 0.0889 - accuracy: 0.9738 - val_loss: 2.0044 - val_accuracy: 0.8141
In [ ]:
ytrain_pred=(model_resnet.predict(X_train) > 0.5).astype("int32")
print('Confusion Matrix of train :\n',confusion_matrix(y_train,ytrain_pred),'\n')
756/756 [==============================] - 28s 37ms/step Confusion Matrix of train : [[16510 0] [ 1457 6214]]
In [ ]:
y_pred=(model_resnet.predict(X_test) > 0.5).astype("int32")
print('Confusion Matrix of test:\n',confusion_matrix(y_test,y_pred),'\n')
189/189 [==============================] - 7s 37ms/step Confusion Matrix of test: [[3886 276] [ 829 1055]]
In [ ]:
print('Classification Report of train:\n',classification_report(y_train,ytrain_pred),'\n')
Classification Report of train:
precision recall f1-score support
0 0.92 1.00 0.96 16510
1 1.00 0.81 0.90 7671
accuracy 0.94 24181
macro avg 0.96 0.91 0.93 24181
weighted avg 0.94 0.94 0.94 24181
In [ ]:
print('Classification Report of test:\n',classification_report(y_test,y_pred),'\n')
Classification Report of test:
precision recall f1-score support
0 0.82 0.93 0.88 4162
1 0.79 0.56 0.66 1884
accuracy 0.82 6046
macro avg 0.81 0.75 0.77 6046
weighted avg 0.81 0.82 0.81 6046
In [ ]:
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train loss', 'Validation loss'], loc='best')
plt.show()
#Validation loss is test loss
In [20]:
from sklearn.metrics import roc_curve
#define metrics
y_pred_keras = model_resnet.predict(X_test).ravel()
fpr_keras, tpr_keras, thresholds_keras = roc_curve(y_test, y_pred_keras)
from sklearn.metrics import auc
auc_keras = auc(fpr_keras, tpr_keras)
plt.plot(fpr_keras, tpr_keras, label='Keras (area = {:.3f})'.format(auc_keras))
plt.xlabel('False positive rate')
plt.ylabel('True positive rate')
plt.title('ROC curve')
plt.show()
142/142 [==============================] - 6s 35ms/step
InceptionNet
In [7]:
IMAGE_SIZE = [128,128]
inceptionNet = tf.keras.applications.InceptionV3(input_shape = IMAGE_SIZE + [3], weights = "imagenet", include_top = False)
for layer in inceptionNet.layers:
layer.trainable = False
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5 87910968/87910968 [==============================] - 1s 0us/step
In [8]:
x = Flatten()(inceptionNet.output)
prediction = Dense(1, activation = "sigmoid")(x)
model_inceptionNet = Model(inputs = inceptionNet.input, outputs = prediction)
model_inceptionNet.summary()
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 128, 128, 3 0 []
)]
conv2d (Conv2D) (None, 63, 63, 32) 864 ['input_1[0][0]']
batch_normalization (BatchNorm (None, 63, 63, 32) 96 ['conv2d[0][0]']
alization)
activation (Activation) (None, 63, 63, 32) 0 ['batch_normalization[0][0]']
conv2d_1 (Conv2D) (None, 61, 61, 32) 9216 ['activation[0][0]']
batch_normalization_1 (BatchNo (None, 61, 61, 32) 96 ['conv2d_1[0][0]']
rmalization)
activation_1 (Activation) (None, 61, 61, 32) 0 ['batch_normalization_1[0][0]']
conv2d_2 (Conv2D) (None, 61, 61, 64) 18432 ['activation_1[0][0]']
batch_normalization_2 (BatchNo (None, 61, 61, 64) 192 ['conv2d_2[0][0]']
rmalization)
activation_2 (Activation) (None, 61, 61, 64) 0 ['batch_normalization_2[0][0]']
max_pooling2d (MaxPooling2D) (None, 30, 30, 64) 0 ['activation_2[0][0]']
conv2d_3 (Conv2D) (None, 30, 30, 80) 5120 ['max_pooling2d[0][0]']
batch_normalization_3 (BatchNo (None, 30, 30, 80) 240 ['conv2d_3[0][0]']
rmalization)
activation_3 (Activation) (None, 30, 30, 80) 0 ['batch_normalization_3[0][0]']
conv2d_4 (Conv2D) (None, 28, 28, 192) 138240 ['activation_3[0][0]']
batch_normalization_4 (BatchNo (None, 28, 28, 192) 576 ['conv2d_4[0][0]']
rmalization)
activation_4 (Activation) (None, 28, 28, 192) 0 ['batch_normalization_4[0][0]']
max_pooling2d_1 (MaxPooling2D) (None, 13, 13, 192) 0 ['activation_4[0][0]']
conv2d_8 (Conv2D) (None, 13, 13, 64) 12288 ['max_pooling2d_1[0][0]']
batch_normalization_8 (BatchNo (None, 13, 13, 64) 192 ['conv2d_8[0][0]']
rmalization)
activation_8 (Activation) (None, 13, 13, 64) 0 ['batch_normalization_8[0][0]']
conv2d_6 (Conv2D) (None, 13, 13, 48) 9216 ['max_pooling2d_1[0][0]']
conv2d_9 (Conv2D) (None, 13, 13, 96) 55296 ['activation_8[0][0]']
batch_normalization_6 (BatchNo (None, 13, 13, 48) 144 ['conv2d_6[0][0]']
rmalization)
batch_normalization_9 (BatchNo (None, 13, 13, 96) 288 ['conv2d_9[0][0]']
rmalization)
activation_6 (Activation) (None, 13, 13, 48) 0 ['batch_normalization_6[0][0]']
activation_9 (Activation) (None, 13, 13, 96) 0 ['batch_normalization_9[0][0]']
average_pooling2d (AveragePool (None, 13, 13, 192) 0 ['max_pooling2d_1[0][0]']
ing2D)
conv2d_5 (Conv2D) (None, 13, 13, 64) 12288 ['max_pooling2d_1[0][0]']
conv2d_7 (Conv2D) (None, 13, 13, 64) 76800 ['activation_6[0][0]']
conv2d_10 (Conv2D) (None, 13, 13, 96) 82944 ['activation_9[0][0]']
conv2d_11 (Conv2D) (None, 13, 13, 32) 6144 ['average_pooling2d[0][0]']
batch_normalization_5 (BatchNo (None, 13, 13, 64) 192 ['conv2d_5[0][0]']
rmalization)
batch_normalization_7 (BatchNo (None, 13, 13, 64) 192 ['conv2d_7[0][0]']
rmalization)
batch_normalization_10 (BatchN (None, 13, 13, 96) 288 ['conv2d_10[0][0]']
ormalization)
batch_normalization_11 (BatchN (None, 13, 13, 32) 96 ['conv2d_11[0][0]']
ormalization)
activation_5 (Activation) (None, 13, 13, 64) 0 ['batch_normalization_5[0][0]']
activation_7 (Activation) (None, 13, 13, 64) 0 ['batch_normalization_7[0][0]']
activation_10 (Activation) (None, 13, 13, 96) 0 ['batch_normalization_10[0][0]']
activation_11 (Activation) (None, 13, 13, 32) 0 ['batch_normalization_11[0][0]']
mixed0 (Concatenate) (None, 13, 13, 256) 0 ['activation_5[0][0]',
'activation_7[0][0]',
'activation_10[0][0]',
'activation_11[0][0]']
conv2d_15 (Conv2D) (None, 13, 13, 64) 16384 ['mixed0[0][0]']
batch_normalization_15 (BatchN (None, 13, 13, 64) 192 ['conv2d_15[0][0]']
ormalization)
activation_15 (Activation) (None, 13, 13, 64) 0 ['batch_normalization_15[0][0]']
conv2d_13 (Conv2D) (None, 13, 13, 48) 12288 ['mixed0[0][0]']
conv2d_16 (Conv2D) (None, 13, 13, 96) 55296 ['activation_15[0][0]']
batch_normalization_13 (BatchN (None, 13, 13, 48) 144 ['conv2d_13[0][0]']
ormalization)
batch_normalization_16 (BatchN (None, 13, 13, 96) 288 ['conv2d_16[0][0]']
ormalization)
activation_13 (Activation) (None, 13, 13, 48) 0 ['batch_normalization_13[0][0]']
activation_16 (Activation) (None, 13, 13, 96) 0 ['batch_normalization_16[0][0]']
average_pooling2d_1 (AveragePo (None, 13, 13, 256) 0 ['mixed0[0][0]']
oling2D)
conv2d_12 (Conv2D) (None, 13, 13, 64) 16384 ['mixed0[0][0]']
conv2d_14 (Conv2D) (None, 13, 13, 64) 76800 ['activation_13[0][0]']
conv2d_17 (Conv2D) (None, 13, 13, 96) 82944 ['activation_16[0][0]']
conv2d_18 (Conv2D) (None, 13, 13, 64) 16384 ['average_pooling2d_1[0][0]']
batch_normalization_12 (BatchN (None, 13, 13, 64) 192 ['conv2d_12[0][0]']
ormalization)
batch_normalization_14 (BatchN (None, 13, 13, 64) 192 ['conv2d_14[0][0]']
ormalization)
batch_normalization_17 (BatchN (None, 13, 13, 96) 288 ['conv2d_17[0][0]']
ormalization)
batch_normalization_18 (BatchN (None, 13, 13, 64) 192 ['conv2d_18[0][0]']
ormalization)
activation_12 (Activation) (None, 13, 13, 64) 0 ['batch_normalization_12[0][0]']
activation_14 (Activation) (None, 13, 13, 64) 0 ['batch_normalization_14[0][0]']
activation_17 (Activation) (None, 13, 13, 96) 0 ['batch_normalization_17[0][0]']
activation_18 (Activation) (None, 13, 13, 64) 0 ['batch_normalization_18[0][0]']
mixed1 (Concatenate) (None, 13, 13, 288) 0 ['activation_12[0][0]',
'activation_14[0][0]',
'activation_17[0][0]',
'activation_18[0][0]']
conv2d_22 (Conv2D) (None, 13, 13, 64) 18432 ['mixed1[0][0]']
batch_normalization_22 (BatchN (None, 13, 13, 64) 192 ['conv2d_22[0][0]']
ormalization)
activation_22 (Activation) (None, 13, 13, 64) 0 ['batch_normalization_22[0][0]']
conv2d_20 (Conv2D) (None, 13, 13, 48) 13824 ['mixed1[0][0]']
conv2d_23 (Conv2D) (None, 13, 13, 96) 55296 ['activation_22[0][0]']
batch_normalization_20 (BatchN (None, 13, 13, 48) 144 ['conv2d_20[0][0]']
ormalization)
batch_normalization_23 (BatchN (None, 13, 13, 96) 288 ['conv2d_23[0][0]']
ormalization)
activation_20 (Activation) (None, 13, 13, 48) 0 ['batch_normalization_20[0][0]']
activation_23 (Activation) (None, 13, 13, 96) 0 ['batch_normalization_23[0][0]']
average_pooling2d_2 (AveragePo (None, 13, 13, 288) 0 ['mixed1[0][0]']
oling2D)
conv2d_19 (Conv2D) (None, 13, 13, 64) 18432 ['mixed1[0][0]']
conv2d_21 (Conv2D) (None, 13, 13, 64) 76800 ['activation_20[0][0]']
conv2d_24 (Conv2D) (None, 13, 13, 96) 82944 ['activation_23[0][0]']
conv2d_25 (Conv2D) (None, 13, 13, 64) 18432 ['average_pooling2d_2[0][0]']
batch_normalization_19 (BatchN (None, 13, 13, 64) 192 ['conv2d_19[0][0]']
ormalization)
batch_normalization_21 (BatchN (None, 13, 13, 64) 192 ['conv2d_21[0][0]']
ormalization)
batch_normalization_24 (BatchN (None, 13, 13, 96) 288 ['conv2d_24[0][0]']
ormalization)
batch_normalization_25 (BatchN (None, 13, 13, 64) 192 ['conv2d_25[0][0]']
ormalization)
activation_19 (Activation) (None, 13, 13, 64) 0 ['batch_normalization_19[0][0]']
activation_21 (Activation) (None, 13, 13, 64) 0 ['batch_normalization_21[0][0]']
activation_24 (Activation) (None, 13, 13, 96) 0 ['batch_normalization_24[0][0]']
activation_25 (Activation) (None, 13, 13, 64) 0 ['batch_normalization_25[0][0]']
mixed2 (Concatenate) (None, 13, 13, 288) 0 ['activation_19[0][0]',
'activation_21[0][0]',
'activation_24[0][0]',
'activation_25[0][0]']
conv2d_27 (Conv2D) (None, 13, 13, 64) 18432 ['mixed2[0][0]']
batch_normalization_27 (BatchN (None, 13, 13, 64) 192 ['conv2d_27[0][0]']
ormalization)
activation_27 (Activation) (None, 13, 13, 64) 0 ['batch_normalization_27[0][0]']
conv2d_28 (Conv2D) (None, 13, 13, 96) 55296 ['activation_27[0][0]']
batch_normalization_28 (BatchN (None, 13, 13, 96) 288 ['conv2d_28[0][0]']
ormalization)
activation_28 (Activation) (None, 13, 13, 96) 0 ['batch_normalization_28[0][0]']
conv2d_26 (Conv2D) (None, 6, 6, 384) 995328 ['mixed2[0][0]']
conv2d_29 (Conv2D) (None, 6, 6, 96) 82944 ['activation_28[0][0]']
batch_normalization_26 (BatchN (None, 6, 6, 384) 1152 ['conv2d_26[0][0]']
ormalization)
batch_normalization_29 (BatchN (None, 6, 6, 96) 288 ['conv2d_29[0][0]']
ormalization)
activation_26 (Activation) (None, 6, 6, 384) 0 ['batch_normalization_26[0][0]']
activation_29 (Activation) (None, 6, 6, 96) 0 ['batch_normalization_29[0][0]']
max_pooling2d_2 (MaxPooling2D) (None, 6, 6, 288) 0 ['mixed2[0][0]']
mixed3 (Concatenate) (None, 6, 6, 768) 0 ['activation_26[0][0]',
'activation_29[0][0]',
'max_pooling2d_2[0][0]']
conv2d_34 (Conv2D) (None, 6, 6, 128) 98304 ['mixed3[0][0]']
batch_normalization_34 (BatchN (None, 6, 6, 128) 384 ['conv2d_34[0][0]']
ormalization)
activation_34 (Activation) (None, 6, 6, 128) 0 ['batch_normalization_34[0][0]']
conv2d_35 (Conv2D) (None, 6, 6, 128) 114688 ['activation_34[0][0]']
batch_normalization_35 (BatchN (None, 6, 6, 128) 384 ['conv2d_35[0][0]']
ormalization)
activation_35 (Activation) (None, 6, 6, 128) 0 ['batch_normalization_35[0][0]']
conv2d_31 (Conv2D) (None, 6, 6, 128) 98304 ['mixed3[0][0]']
conv2d_36 (Conv2D) (None, 6, 6, 128) 114688 ['activation_35[0][0]']
batch_normalization_31 (BatchN (None, 6, 6, 128) 384 ['conv2d_31[0][0]']
ormalization)
batch_normalization_36 (BatchN (None, 6, 6, 128) 384 ['conv2d_36[0][0]']
ormalization)
activation_31 (Activation) (None, 6, 6, 128) 0 ['batch_normalization_31[0][0]']
activation_36 (Activation) (None, 6, 6, 128) 0 ['batch_normalization_36[0][0]']
conv2d_32 (Conv2D) (None, 6, 6, 128) 114688 ['activation_31[0][0]']
conv2d_37 (Conv2D) (None, 6, 6, 128) 114688 ['activation_36[0][0]']
batch_normalization_32 (BatchN (None, 6, 6, 128) 384 ['conv2d_32[0][0]']
ormalization)
batch_normalization_37 (BatchN (None, 6, 6, 128) 384 ['conv2d_37[0][0]']
ormalization)
activation_32 (Activation) (None, 6, 6, 128) 0 ['batch_normalization_32[0][0]']
activation_37 (Activation) (None, 6, 6, 128) 0 ['batch_normalization_37[0][0]']
average_pooling2d_3 (AveragePo (None, 6, 6, 768) 0 ['mixed3[0][0]']
oling2D)
conv2d_30 (Conv2D) (None, 6, 6, 192) 147456 ['mixed3[0][0]']
conv2d_33 (Conv2D) (None, 6, 6, 192) 172032 ['activation_32[0][0]']
conv2d_38 (Conv2D) (None, 6, 6, 192) 172032 ['activation_37[0][0]']
conv2d_39 (Conv2D) (None, 6, 6, 192) 147456 ['average_pooling2d_3[0][0]']
batch_normalization_30 (BatchN (None, 6, 6, 192) 576 ['conv2d_30[0][0]']
ormalization)
batch_normalization_33 (BatchN (None, 6, 6, 192) 576 ['conv2d_33[0][0]']
ormalization)
batch_normalization_38 (BatchN (None, 6, 6, 192) 576 ['conv2d_38[0][0]']
ormalization)
batch_normalization_39 (BatchN (None, 6, 6, 192) 576 ['conv2d_39[0][0]']
ormalization)
activation_30 (Activation) (None, 6, 6, 192) 0 ['batch_normalization_30[0][0]']
activation_33 (Activation) (None, 6, 6, 192) 0 ['batch_normalization_33[0][0]']
activation_38 (Activation) (None, 6, 6, 192) 0 ['batch_normalization_38[0][0]']
activation_39 (Activation) (None, 6, 6, 192) 0 ['batch_normalization_39[0][0]']
mixed4 (Concatenate) (None, 6, 6, 768) 0 ['activation_30[0][0]',
'activation_33[0][0]',
'activation_38[0][0]',
'activation_39[0][0]']
conv2d_44 (Conv2D) (None, 6, 6, 160) 122880 ['mixed4[0][0]']
batch_normalization_44 (BatchN (None, 6, 6, 160) 480 ['conv2d_44[0][0]']
ormalization)
activation_44 (Activation) (None, 6, 6, 160) 0 ['batch_normalization_44[0][0]']
conv2d_45 (Conv2D) (None, 6, 6, 160) 179200 ['activation_44[0][0]']
batch_normalization_45 (BatchN (None, 6, 6, 160) 480 ['conv2d_45[0][0]']
ormalization)
activation_45 (Activation) (None, 6, 6, 160) 0 ['batch_normalization_45[0][0]']
conv2d_41 (Conv2D) (None, 6, 6, 160) 122880 ['mixed4[0][0]']
conv2d_46 (Conv2D) (None, 6, 6, 160) 179200 ['activation_45[0][0]']
batch_normalization_41 (BatchN (None, 6, 6, 160) 480 ['conv2d_41[0][0]']
ormalization)
batch_normalization_46 (BatchN (None, 6, 6, 160) 480 ['conv2d_46[0][0]']
ormalization)
activation_41 (Activation) (None, 6, 6, 160) 0 ['batch_normalization_41[0][0]']
activation_46 (Activation) (None, 6, 6, 160) 0 ['batch_normalization_46[0][0]']
conv2d_42 (Conv2D) (None, 6, 6, 160) 179200 ['activation_41[0][0]']
conv2d_47 (Conv2D) (None, 6, 6, 160) 179200 ['activation_46[0][0]']
batch_normalization_42 (BatchN (None, 6, 6, 160) 480 ['conv2d_42[0][0]']
ormalization)
batch_normalization_47 (BatchN (None, 6, 6, 160) 480 ['conv2d_47[0][0]']
ormalization)
activation_42 (Activation) (None, 6, 6, 160) 0 ['batch_normalization_42[0][0]']
activation_47 (Activation) (None, 6, 6, 160) 0 ['batch_normalization_47[0][0]']
average_pooling2d_4 (AveragePo (None, 6, 6, 768) 0 ['mixed4[0][0]']
oling2D)
conv2d_40 (Conv2D) (None, 6, 6, 192) 147456 ['mixed4[0][0]']
conv2d_43 (Conv2D) (None, 6, 6, 192) 215040 ['activation_42[0][0]']
conv2d_48 (Conv2D) (None, 6, 6, 192) 215040 ['activation_47[0][0]']
conv2d_49 (Conv2D) (None, 6, 6, 192) 147456 ['average_pooling2d_4[0][0]']
batch_normalization_40 (BatchN (None, 6, 6, 192) 576 ['conv2d_40[0][0]']
ormalization)
batch_normalization_43 (BatchN (None, 6, 6, 192) 576 ['conv2d_43[0][0]']
ormalization)
batch_normalization_48 (BatchN (None, 6, 6, 192) 576 ['conv2d_48[0][0]']
ormalization)
batch_normalization_49 (BatchN (None, 6, 6, 192) 576 ['conv2d_49[0][0]']
ormalization)
activation_40 (Activation) (None, 6, 6, 192) 0 ['batch_normalization_40[0][0]']
activation_43 (Activation) (None, 6, 6, 192) 0 ['batch_normalization_43[0][0]']
activation_48 (Activation) (None, 6, 6, 192) 0 ['batch_normalization_48[0][0]']
activation_49 (Activation) (None, 6, 6, 192) 0 ['batch_normalization_49[0][0]']
mixed5 (Concatenate) (None, 6, 6, 768) 0 ['activation_40[0][0]',
'activation_43[0][0]',
'activation_48[0][0]',
'activation_49[0][0]']
conv2d_54 (Conv2D) (None, 6, 6, 160) 122880 ['mixed5[0][0]']
batch_normalization_54 (BatchN (None, 6, 6, 160) 480 ['conv2d_54[0][0]']
ormalization)
activation_54 (Activation) (None, 6, 6, 160) 0 ['batch_normalization_54[0][0]']
conv2d_55 (Conv2D) (None, 6, 6, 160) 179200 ['activation_54[0][0]']
batch_normalization_55 (BatchN (None, 6, 6, 160) 480 ['conv2d_55[0][0]']
ormalization)
activation_55 (Activation) (None, 6, 6, 160) 0 ['batch_normalization_55[0][0]']
conv2d_51 (Conv2D) (None, 6, 6, 160) 122880 ['mixed5[0][0]']
conv2d_56 (Conv2D) (None, 6, 6, 160) 179200 ['activation_55[0][0]']
batch_normalization_51 (BatchN (None, 6, 6, 160) 480 ['conv2d_51[0][0]']
ormalization)
batch_normalization_56 (BatchN (None, 6, 6, 160) 480 ['conv2d_56[0][0]']
ormalization)
activation_51 (Activation) (None, 6, 6, 160) 0 ['batch_normalization_51[0][0]']
activation_56 (Activation) (None, 6, 6, 160) 0 ['batch_normalization_56[0][0]']
conv2d_52 (Conv2D) (None, 6, 6, 160) 179200 ['activation_51[0][0]']
conv2d_57 (Conv2D) (None, 6, 6, 160) 179200 ['activation_56[0][0]']
batch_normalization_52 (BatchN (None, 6, 6, 160) 480 ['conv2d_52[0][0]']
ormalization)
batch_normalization_57 (BatchN (None, 6, 6, 160) 480 ['conv2d_57[0][0]']
ormalization)
activation_52 (Activation) (None, 6, 6, 160) 0 ['batch_normalization_52[0][0]']
activation_57 (Activation) (None, 6, 6, 160) 0 ['batch_normalization_57[0][0]']
average_pooling2d_5 (AveragePo (None, 6, 6, 768) 0 ['mixed5[0][0]']
oling2D)
conv2d_50 (Conv2D) (None, 6, 6, 192) 147456 ['mixed5[0][0]']
conv2d_53 (Conv2D) (None, 6, 6, 192) 215040 ['activation_52[0][0]']
conv2d_58 (Conv2D) (None, 6, 6, 192) 215040 ['activation_57[0][0]']
conv2d_59 (Conv2D) (None, 6, 6, 192) 147456 ['average_pooling2d_5[0][0]']
batch_normalization_50 (BatchN (None, 6, 6, 192) 576 ['conv2d_50[0][0]']
ormalization)
batch_normalization_53 (BatchN (None, 6, 6, 192) 576 ['conv2d_53[0][0]']
ormalization)
batch_normalization_58 (BatchN (None, 6, 6, 192) 576 ['conv2d_58[0][0]']
ormalization)
batch_normalization_59 (BatchN (None, 6, 6, 192) 576 ['conv2d_59[0][0]']
ormalization)
activation_50 (Activation) (None, 6, 6, 192) 0 ['batch_normalization_50[0][0]']
activation_53 (Activation) (None, 6, 6, 192) 0 ['batch_normalization_53[0][0]']
activation_58 (Activation) (None, 6, 6, 192) 0 ['batch_normalization_58[0][0]']
activation_59 (Activation) (None, 6, 6, 192) 0 ['batch_normalization_59[0][0]']
mixed6 (Concatenate) (None, 6, 6, 768) 0 ['activation_50[0][0]',
'activation_53[0][0]',
'activation_58[0][0]',
'activation_59[0][0]']
conv2d_64 (Conv2D) (None, 6, 6, 192) 147456 ['mixed6[0][0]']
batch_normalization_64 (BatchN (None, 6, 6, 192) 576 ['conv2d_64[0][0]']
ormalization)
activation_64 (Activation) (None, 6, 6, 192) 0 ['batch_normalization_64[0][0]']
conv2d_65 (Conv2D) (None, 6, 6, 192) 258048 ['activation_64[0][0]']
batch_normalization_65 (BatchN (None, 6, 6, 192) 576 ['conv2d_65[0][0]']
ormalization)
activation_65 (Activation) (None, 6, 6, 192) 0 ['batch_normalization_65[0][0]']
conv2d_61 (Conv2D) (None, 6, 6, 192) 147456 ['mixed6[0][0]']
conv2d_66 (Conv2D) (None, 6, 6, 192) 258048 ['activation_65[0][0]']
batch_normalization_61 (BatchN (None, 6, 6, 192) 576 ['conv2d_61[0][0]']
ormalization)
batch_normalization_66 (BatchN (None, 6, 6, 192) 576 ['conv2d_66[0][0]']
ormalization)
activation_61 (Activation) (None, 6, 6, 192) 0 ['batch_normalization_61[0][0]']
activation_66 (Activation) (None, 6, 6, 192) 0 ['batch_normalization_66[0][0]']
conv2d_62 (Conv2D) (None, 6, 6, 192) 258048 ['activation_61[0][0]']
conv2d_67 (Conv2D) (None, 6, 6, 192) 258048 ['activation_66[0][0]']
batch_normalization_62 (BatchN (None, 6, 6, 192) 576 ['conv2d_62[0][0]']
ormalization)
batch_normalization_67 (BatchN (None, 6, 6, 192) 576 ['conv2d_67[0][0]']
ormalization)
activation_62 (Activation) (None, 6, 6, 192) 0 ['batch_normalization_62[0][0]']
activation_67 (Activation) (None, 6, 6, 192) 0 ['batch_normalization_67[0][0]']
average_pooling2d_6 (AveragePo (None, 6, 6, 768) 0 ['mixed6[0][0]']
oling2D)
conv2d_60 (Conv2D) (None, 6, 6, 192) 147456 ['mixed6[0][0]']
conv2d_63 (Conv2D) (None, 6, 6, 192) 258048 ['activation_62[0][0]']
conv2d_68 (Conv2D) (None, 6, 6, 192) 258048 ['activation_67[0][0]']
conv2d_69 (Conv2D) (None, 6, 6, 192) 147456 ['average_pooling2d_6[0][0]']
batch_normalization_60 (BatchN (None, 6, 6, 192) 576 ['conv2d_60[0][0]']
ormalization)
batch_normalization_63 (BatchN (None, 6, 6, 192) 576 ['conv2d_63[0][0]']
ormalization)
batch_normalization_68 (BatchN (None, 6, 6, 192) 576 ['conv2d_68[0][0]']
ormalization)
batch_normalization_69 (BatchN (None, 6, 6, 192) 576 ['conv2d_69[0][0]']
ormalization)
activation_60 (Activation) (None, 6, 6, 192) 0 ['batch_normalization_60[0][0]']
activation_63 (Activation) (None, 6, 6, 192) 0 ['batch_normalization_63[0][0]']
activation_68 (Activation) (None, 6, 6, 192) 0 ['batch_normalization_68[0][0]']
activation_69 (Activation) (None, 6, 6, 192) 0 ['batch_normalization_69[0][0]']
mixed7 (Concatenate) (None, 6, 6, 768) 0 ['activation_60[0][0]',
'activation_63[0][0]',
'activation_68[0][0]',
'activation_69[0][0]']
conv2d_72 (Conv2D) (None, 6, 6, 192) 147456 ['mixed7[0][0]']
batch_normalization_72 (BatchN (None, 6, 6, 192) 576 ['conv2d_72[0][0]']
ormalization)
activation_72 (Activation) (None, 6, 6, 192) 0 ['batch_normalization_72[0][0]']
conv2d_73 (Conv2D) (None, 6, 6, 192) 258048 ['activation_72[0][0]']
batch_normalization_73 (BatchN (None, 6, 6, 192) 576 ['conv2d_73[0][0]']
ormalization)
activation_73 (Activation) (None, 6, 6, 192) 0 ['batch_normalization_73[0][0]']
conv2d_70 (Conv2D) (None, 6, 6, 192) 147456 ['mixed7[0][0]']
conv2d_74 (Conv2D) (None, 6, 6, 192) 258048 ['activation_73[0][0]']
batch_normalization_70 (BatchN (None, 6, 6, 192) 576 ['conv2d_70[0][0]']
ormalization)
batch_normalization_74 (BatchN (None, 6, 6, 192) 576 ['conv2d_74[0][0]']
ormalization)
activation_70 (Activation) (None, 6, 6, 192) 0 ['batch_normalization_70[0][0]']
activation_74 (Activation) (None, 6, 6, 192) 0 ['batch_normalization_74[0][0]']
conv2d_71 (Conv2D) (None, 2, 2, 320) 552960 ['activation_70[0][0]']
conv2d_75 (Conv2D) (None, 2, 2, 192) 331776 ['activation_74[0][0]']
batch_normalization_71 (BatchN (None, 2, 2, 320) 960 ['conv2d_71[0][0]']
ormalization)
batch_normalization_75 (BatchN (None, 2, 2, 192) 576 ['conv2d_75[0][0]']
ormalization)
activation_71 (Activation) (None, 2, 2, 320) 0 ['batch_normalization_71[0][0]']
activation_75 (Activation) (None, 2, 2, 192) 0 ['batch_normalization_75[0][0]']
max_pooling2d_3 (MaxPooling2D) (None, 2, 2, 768) 0 ['mixed7[0][0]']
mixed8 (Concatenate) (None, 2, 2, 1280) 0 ['activation_71[0][0]',
'activation_75[0][0]',
'max_pooling2d_3[0][0]']
conv2d_80 (Conv2D) (None, 2, 2, 448) 573440 ['mixed8[0][0]']
batch_normalization_80 (BatchN (None, 2, 2, 448) 1344 ['conv2d_80[0][0]']
ormalization)
activation_80 (Activation) (None, 2, 2, 448) 0 ['batch_normalization_80[0][0]']
conv2d_77 (Conv2D) (None, 2, 2, 384) 491520 ['mixed8[0][0]']
conv2d_81 (Conv2D) (None, 2, 2, 384) 1548288 ['activation_80[0][0]']
batch_normalization_77 (BatchN (None, 2, 2, 384) 1152 ['conv2d_77[0][0]']
ormalization)
batch_normalization_81 (BatchN (None, 2, 2, 384) 1152 ['conv2d_81[0][0]']
ormalization)
activation_77 (Activation) (None, 2, 2, 384) 0 ['batch_normalization_77[0][0]']
activation_81 (Activation) (None, 2, 2, 384) 0 ['batch_normalization_81[0][0]']
conv2d_78 (Conv2D) (None, 2, 2, 384) 442368 ['activation_77[0][0]']
conv2d_79 (Conv2D) (None, 2, 2, 384) 442368 ['activation_77[0][0]']
conv2d_82 (Conv2D) (None, 2, 2, 384) 442368 ['activation_81[0][0]']
conv2d_83 (Conv2D) (None, 2, 2, 384) 442368 ['activation_81[0][0]']
average_pooling2d_7 (AveragePo (None, 2, 2, 1280) 0 ['mixed8[0][0]']
oling2D)
conv2d_76 (Conv2D) (None, 2, 2, 320) 409600 ['mixed8[0][0]']
batch_normalization_78 (BatchN (None, 2, 2, 384) 1152 ['conv2d_78[0][0]']
ormalization)
batch_normalization_79 (BatchN (None, 2, 2, 384) 1152 ['conv2d_79[0][0]']
ormalization)
batch_normalization_82 (BatchN (None, 2, 2, 384) 1152 ['conv2d_82[0][0]']
ormalization)
batch_normalization_83 (BatchN (None, 2, 2, 384) 1152 ['conv2d_83[0][0]']
ormalization)
conv2d_84 (Conv2D) (None, 2, 2, 192) 245760 ['average_pooling2d_7[0][0]']
batch_normalization_76 (BatchN (None, 2, 2, 320) 960 ['conv2d_76[0][0]']
ormalization)
activation_78 (Activation) (None, 2, 2, 384) 0 ['batch_normalization_78[0][0]']
activation_79 (Activation) (None, 2, 2, 384) 0 ['batch_normalization_79[0][0]']
activation_82 (Activation) (None, 2, 2, 384) 0 ['batch_normalization_82[0][0]']
activation_83 (Activation) (None, 2, 2, 384) 0 ['batch_normalization_83[0][0]']
batch_normalization_84 (BatchN (None, 2, 2, 192) 576 ['conv2d_84[0][0]']
ormalization)
activation_76 (Activation) (None, 2, 2, 320) 0 ['batch_normalization_76[0][0]']
mixed9_0 (Concatenate) (None, 2, 2, 768) 0 ['activation_78[0][0]',
'activation_79[0][0]']
concatenate (Concatenate) (None, 2, 2, 768) 0 ['activation_82[0][0]',
'activation_83[0][0]']
activation_84 (Activation) (None, 2, 2, 192) 0 ['batch_normalization_84[0][0]']
mixed9 (Concatenate) (None, 2, 2, 2048) 0 ['activation_76[0][0]',
'mixed9_0[0][0]',
'concatenate[0][0]',
'activation_84[0][0]']
conv2d_89 (Conv2D) (None, 2, 2, 448) 917504 ['mixed9[0][0]']
batch_normalization_89 (BatchN (None, 2, 2, 448) 1344 ['conv2d_89[0][0]']
ormalization)
activation_89 (Activation) (None, 2, 2, 448) 0 ['batch_normalization_89[0][0]']
conv2d_86 (Conv2D) (None, 2, 2, 384) 786432 ['mixed9[0][0]']
conv2d_90 (Conv2D) (None, 2, 2, 384) 1548288 ['activation_89[0][0]']
batch_normalization_86 (BatchN (None, 2, 2, 384) 1152 ['conv2d_86[0][0]']
ormalization)
batch_normalization_90 (BatchN (None, 2, 2, 384) 1152 ['conv2d_90[0][0]']
ormalization)
activation_86 (Activation) (None, 2, 2, 384) 0 ['batch_normalization_86[0][0]']
activation_90 (Activation) (None, 2, 2, 384) 0 ['batch_normalization_90[0][0]']
conv2d_87 (Conv2D) (None, 2, 2, 384) 442368 ['activation_86[0][0]']
conv2d_88 (Conv2D) (None, 2, 2, 384) 442368 ['activation_86[0][0]']
conv2d_91 (Conv2D) (None, 2, 2, 384) 442368 ['activation_90[0][0]']
conv2d_92 (Conv2D) (None, 2, 2, 384) 442368 ['activation_90[0][0]']
average_pooling2d_8 (AveragePo (None, 2, 2, 2048) 0 ['mixed9[0][0]']
oling2D)
conv2d_85 (Conv2D) (None, 2, 2, 320) 655360 ['mixed9[0][0]']
batch_normalization_87 (BatchN (None, 2, 2, 384) 1152 ['conv2d_87[0][0]']
ormalization)
batch_normalization_88 (BatchN (None, 2, 2, 384) 1152 ['conv2d_88[0][0]']
ormalization)
batch_normalization_91 (BatchN (None, 2, 2, 384) 1152 ['conv2d_91[0][0]']
ormalization)
batch_normalization_92 (BatchN (None, 2, 2, 384) 1152 ['conv2d_92[0][0]']
ormalization)
conv2d_93 (Conv2D) (None, 2, 2, 192) 393216 ['average_pooling2d_8[0][0]']
batch_normalization_85 (BatchN (None, 2, 2, 320) 960 ['conv2d_85[0][0]']
ormalization)
activation_87 (Activation) (None, 2, 2, 384) 0 ['batch_normalization_87[0][0]']
activation_88 (Activation) (None, 2, 2, 384) 0 ['batch_normalization_88[0][0]']
activation_91 (Activation) (None, 2, 2, 384) 0 ['batch_normalization_91[0][0]']
activation_92 (Activation) (None, 2, 2, 384) 0 ['batch_normalization_92[0][0]']
batch_normalization_93 (BatchN (None, 2, 2, 192) 576 ['conv2d_93[0][0]']
ormalization)
activation_85 (Activation) (None, 2, 2, 320) 0 ['batch_normalization_85[0][0]']
mixed9_1 (Concatenate) (None, 2, 2, 768) 0 ['activation_87[0][0]',
'activation_88[0][0]']
concatenate_1 (Concatenate) (None, 2, 2, 768) 0 ['activation_91[0][0]',
'activation_92[0][0]']
activation_93 (Activation) (None, 2, 2, 192) 0 ['batch_normalization_93[0][0]']
mixed10 (Concatenate) (None, 2, 2, 2048) 0 ['activation_85[0][0]',
'mixed9_1[0][0]',
'concatenate_1[0][0]',
'activation_93[0][0]']
flatten (Flatten) (None, 8192) 0 ['mixed10[0][0]']
dense (Dense) (None, 1) 8193 ['flatten[0][0]']
==================================================================================================
Total params: 21,810,977
Trainable params: 8,193
Non-trainable params: 21,802,784
__________________________________________________________________________________________________
In [9]:
model_inceptionNet.compile(loss = "binary_crossentropy",
optimizer = "adam",
metrics = ["accuracy"])
In [11]:
history=model_inceptionNet.fit(X_train,y_train,
validation_data=(X_test,y_test),
epochs=5,
batch_size=32)
Epoch 1/5 662/662 [==============================] - 28s 36ms/step - loss: 7.0997 - accuracy: 0.7068 - val_loss: 5.2263 - val_accuracy: 0.7667 Epoch 2/5 662/662 [==============================] - 22s 33ms/step - loss: 6.0465 - accuracy: 0.7160 - val_loss: 5.9041 - val_accuracy: 0.7585 Epoch 3/5 662/662 [==============================] - 22s 33ms/step - loss: 5.4998 - accuracy: 0.7294 - val_loss: 6.3901 - val_accuracy: 0.7135 Epoch 4/5 662/662 [==============================] - 22s 33ms/step - loss: 5.8539 - accuracy: 0.7309 - val_loss: 6.1762 - val_accuracy: 0.7640 Epoch 5/5 662/662 [==============================] - 22s 33ms/step - loss: 5.6806 - accuracy: 0.7332 - val_loss: 6.0937 - val_accuracy: 0.7750
In [ ]:
ytrain_pred=(model_inceptionNet.predict(X_train) > 0.5).astype("int32")
print('Confusion Matrix of train :\n',confusion_matrix(y_train,ytrain_pred),'\n')
756/756 [==============================] - 657s 867ms/step Confusion Matrix of train : [[14560 1950] [ 2216 5455]]
In [ ]:
y_pred=(model_inceptionNet.predict(X_test) > 0.5).astype("int32")
print('Confusion Matrix of test:\n',confusion_matrix(y_test,y_pred),'\n')
189/189 [==============================] - 168s 884ms/step Confusion Matrix of test: [[3389 773] [ 528 1356]]
In [ ]:
print('Classification Report of train:\n',classification_report(y_train,ytrain_pred),'\n')
Classification Report of train:
precision recall f1-score support
0 0.87 0.88 0.87 16510
1 0.74 0.71 0.72 7671
accuracy 0.83 24181
macro avg 0.80 0.80 0.80 24181
weighted avg 0.83 0.83 0.83 24181
In [ ]:
print('Classification Report of test:\n',classification_report(y_test,y_pred),'\n')
Classification Report of test:
precision recall f1-score support
0 0.87 0.81 0.84 4162
1 0.64 0.72 0.68 1884
accuracy 0.78 6046
macro avg 0.75 0.77 0.76 6046
weighted avg 0.79 0.78 0.79 6046
In [ ]:
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train loss', 'Validation loss'], loc='best')
plt.show()
#Validation loss is test loss
In [12]:
from sklearn.metrics import roc_curve
#define metrics
y_pred_keras = model_inceptionNet.predict(X_test).ravel()
fpr_keras, tpr_keras, thresholds_keras = roc_curve(y_test, y_pred_keras)
from sklearn.metrics import auc
auc_keras = auc(fpr_keras, tpr_keras)
plt.plot(fpr_keras, tpr_keras, label='Keras (area = {:.3f})'.format(auc_keras))
plt.xlabel('False positive rate')
plt.ylabel('True positive rate')
plt.title('ROC curve')
plt.show()
142/142 [==============================] - 3s 24ms/step
Step 3: Design, train and test RCNN & its hybrids based object detection models to impose the bounding box or mask over the area of interest. [ 8 points ]
In [ ]:
!unzip /content/drive/MyDrive/stage_2_train_images.zip
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!unzip /content/drive/MyDrive/stage_2_test_images.zip
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In [ ]:
filenames = {}
filenames = os.listdir('/content/stage_2_train_images')
file_count = len(filenames)
random.shuffle(filenames)
validation_data_len = int(file_count * 0.3)
train_data_filenames = filenames[validation_data_len:file_count]
validation_data_filenames = filenames[:validation_data_len]
print('n train samples', len(train_data_filenames))
print('n valid samples', len(validation_data_filenames))
train_data_len = file_count - validation_data_len
image_dimension = 128
print('Image Dimension to use:',image_dimension)
print('sample file:',filenames[0])
n train samples 18679 n valid samples 8005 Image Dimension to use: 128 sample file: fd5d1c97-9947-4703-bb8c-b5e5daeac9fe.dcm
In [ ]:
pneumonia_location_coordinates = {}
with open('drive/My Drive/cv capstone/stage_2_train_labels.csv') as infile:
reader = csv.reader(infile)
next(reader, None)
for rows in reader:
filename = rows[0]
coordinates = rows[1:5]
pneumonia = rows[5]
if pneumonia == '1':
coordinates = [int(float(i)) for i in coordinates]
if filename in pneumonia_location_coordinates:
pneumonia_location_coordinates[filename].append(coordinates)
else:
pneumonia_location_coordinates[filename] = [coordinates]
In [ ]:
def add_downsample_layer(num_of_channels, inputs):
x = keras.layers.BatchNormalization(momentum=0.9)(inputs)
x = keras.layers.LeakyReLU(0)(x)
x = keras.layers.Conv2D(num_of_channels, 1, padding='same', use_bias=False)(x)
x = keras.layers.MaxPool2D(2)(x)
return x
def add_residual_layer(num_of_channels, inputs):
x = keras.layers.BatchNormalization(momentum=0.9)(inputs)
x = keras.layers.LeakyReLU(0)(x)
x = keras.layers.Conv2D(num_of_channels, 3, padding='same', use_bias=False)(x)
x = keras.layers.BatchNormalization(momentum=0.9)(x)
x = keras.layers.LeakyReLU(0)(x)
x = keras.layers.Conv2D(num_of_channels, 3, padding='same', use_bias=False)(x)
return keras.layers.add([x, inputs])
In [ ]:
import keras
def create_network(input_size, num_of_channels, num_of_blocks, depth):
inputs = keras.Input(shape=(input_size, input_size, 1))
x = keras.layers.Conv2D(num_of_channels, 3, padding='same', use_bias=False)(inputs)
for d in range(depth):
num_of_channels = num_of_channels * 2
x = add_downsample_layer(num_of_channels, x)
for b in range(num_of_blocks):
x = add_residual_layer(num_of_channels, x)
x = keras.layers.BatchNormalization(momentum=0.9)(x)
x = keras.layers.LeakyReLU(0)(x)
x = keras.layers.Conv2D(1, 1, activation='sigmoid')(x)
outputs = keras.layers.UpSampling2D(2**depth)(x)
model = keras.Model(inputs=inputs, outputs=outputs)
return model
In [ ]:
def iou_bce_loss(y_true, y_pred):
y_true = tf.reshape(y_true, [-1])
y_pred = tf.reshape(y_pred, [-1])
intersection = tf.reduce_sum(y_true * y_pred)
score = (intersection + 1.) / (tf.reduce_sum(y_true) + tf.reduce_sum(y_pred) - intersection + 1.)
jaccard_loss = 1 - score
return 0.5 * keras.losses.binary_crossentropy(y_true, y_pred) + 0.5 * jaccard_loss
def mean_iou(y_true, y_pred):
y_pred = tf.round(y_pred)
intersect = tf.reduce_sum(y_true * y_pred, axis=[1, 2, 3])
union = tf.reduce_sum(y_true, axis=[1, 2, 3]) + tf.reduce_sum(y_pred, axis=[1, 2, 3])
smooth = tf.ones(tf.shape(intersect))
return tf.reduce_mean((intersect + smooth) / (union - intersect + smooth))
model = create_network(input_size=image_dimension, num_of_channels=32, num_of_blocks=2, depth=4)
model.compile(optimizer='adam',loss=iou_bce_loss,metrics=['accuracy', mean_iou])
model.summary()
def cosine_annealing(x):
lr = 0.001
epochs = 25
return lr* (np.cos(np.pi*x/epochs)+1.) /2
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 128, 128, 1 0 []
)]
conv2d (Conv2D) (None, 128, 128, 32 288 ['input_1[0][0]']
)
batch_normalization (BatchNorm (None, 128, 128, 32 128 ['conv2d[0][0]']
alization) )
leaky_re_lu (LeakyReLU) (None, 128, 128, 32 0 ['batch_normalization[0][0]']
)
conv2d_1 (Conv2D) (None, 128, 128, 64 2048 ['leaky_re_lu[0][0]']
)
max_pooling2d (MaxPooling2D) (None, 64, 64, 64) 0 ['conv2d_1[0][0]']
batch_normalization_1 (BatchNo (None, 64, 64, 64) 256 ['max_pooling2d[0][0]']
rmalization)
leaky_re_lu_1 (LeakyReLU) (None, 64, 64, 64) 0 ['batch_normalization_1[0][0]']
conv2d_2 (Conv2D) (None, 64, 64, 64) 36864 ['leaky_re_lu_1[0][0]']
batch_normalization_2 (BatchNo (None, 64, 64, 64) 256 ['conv2d_2[0][0]']
rmalization)
leaky_re_lu_2 (LeakyReLU) (None, 64, 64, 64) 0 ['batch_normalization_2[0][0]']
conv2d_3 (Conv2D) (None, 64, 64, 64) 36864 ['leaky_re_lu_2[0][0]']
add (Add) (None, 64, 64, 64) 0 ['conv2d_3[0][0]',
'max_pooling2d[0][0]']
batch_normalization_3 (BatchNo (None, 64, 64, 64) 256 ['add[0][0]']
rmalization)
leaky_re_lu_3 (LeakyReLU) (None, 64, 64, 64) 0 ['batch_normalization_3[0][0]']
conv2d_4 (Conv2D) (None, 64, 64, 64) 36864 ['leaky_re_lu_3[0][0]']
batch_normalization_4 (BatchNo (None, 64, 64, 64) 256 ['conv2d_4[0][0]']
rmalization)
leaky_re_lu_4 (LeakyReLU) (None, 64, 64, 64) 0 ['batch_normalization_4[0][0]']
conv2d_5 (Conv2D) (None, 64, 64, 64) 36864 ['leaky_re_lu_4[0][0]']
add_1 (Add) (None, 64, 64, 64) 0 ['conv2d_5[0][0]',
'add[0][0]']
batch_normalization_5 (BatchNo (None, 64, 64, 64) 256 ['add_1[0][0]']
rmalization)
leaky_re_lu_5 (LeakyReLU) (None, 64, 64, 64) 0 ['batch_normalization_5[0][0]']
conv2d_6 (Conv2D) (None, 64, 64, 128) 8192 ['leaky_re_lu_5[0][0]']
max_pooling2d_1 (MaxPooling2D) (None, 32, 32, 128) 0 ['conv2d_6[0][0]']
batch_normalization_6 (BatchNo (None, 32, 32, 128) 512 ['max_pooling2d_1[0][0]']
rmalization)
leaky_re_lu_6 (LeakyReLU) (None, 32, 32, 128) 0 ['batch_normalization_6[0][0]']
conv2d_7 (Conv2D) (None, 32, 32, 128) 147456 ['leaky_re_lu_6[0][0]']
batch_normalization_7 (BatchNo (None, 32, 32, 128) 512 ['conv2d_7[0][0]']
rmalization)
leaky_re_lu_7 (LeakyReLU) (None, 32, 32, 128) 0 ['batch_normalization_7[0][0]']
conv2d_8 (Conv2D) (None, 32, 32, 128) 147456 ['leaky_re_lu_7[0][0]']
add_2 (Add) (None, 32, 32, 128) 0 ['conv2d_8[0][0]',
'max_pooling2d_1[0][0]']
batch_normalization_8 (BatchNo (None, 32, 32, 128) 512 ['add_2[0][0]']
rmalization)
leaky_re_lu_8 (LeakyReLU) (None, 32, 32, 128) 0 ['batch_normalization_8[0][0]']
conv2d_9 (Conv2D) (None, 32, 32, 128) 147456 ['leaky_re_lu_8[0][0]']
batch_normalization_9 (BatchNo (None, 32, 32, 128) 512 ['conv2d_9[0][0]']
rmalization)
leaky_re_lu_9 (LeakyReLU) (None, 32, 32, 128) 0 ['batch_normalization_9[0][0]']
conv2d_10 (Conv2D) (None, 32, 32, 128) 147456 ['leaky_re_lu_9[0][0]']
add_3 (Add) (None, 32, 32, 128) 0 ['conv2d_10[0][0]',
'add_2[0][0]']
batch_normalization_10 (BatchN (None, 32, 32, 128) 512 ['add_3[0][0]']
ormalization)
leaky_re_lu_10 (LeakyReLU) (None, 32, 32, 128) 0 ['batch_normalization_10[0][0]']
conv2d_11 (Conv2D) (None, 32, 32, 256) 32768 ['leaky_re_lu_10[0][0]']
max_pooling2d_2 (MaxPooling2D) (None, 16, 16, 256) 0 ['conv2d_11[0][0]']
batch_normalization_11 (BatchN (None, 16, 16, 256) 1024 ['max_pooling2d_2[0][0]']
ormalization)
leaky_re_lu_11 (LeakyReLU) (None, 16, 16, 256) 0 ['batch_normalization_11[0][0]']
conv2d_12 (Conv2D) (None, 16, 16, 256) 589824 ['leaky_re_lu_11[0][0]']
batch_normalization_12 (BatchN (None, 16, 16, 256) 1024 ['conv2d_12[0][0]']
ormalization)
leaky_re_lu_12 (LeakyReLU) (None, 16, 16, 256) 0 ['batch_normalization_12[0][0]']
conv2d_13 (Conv2D) (None, 16, 16, 256) 589824 ['leaky_re_lu_12[0][0]']
add_4 (Add) (None, 16, 16, 256) 0 ['conv2d_13[0][0]',
'max_pooling2d_2[0][0]']
batch_normalization_13 (BatchN (None, 16, 16, 256) 1024 ['add_4[0][0]']
ormalization)
leaky_re_lu_13 (LeakyReLU) (None, 16, 16, 256) 0 ['batch_normalization_13[0][0]']
conv2d_14 (Conv2D) (None, 16, 16, 256) 589824 ['leaky_re_lu_13[0][0]']
batch_normalization_14 (BatchN (None, 16, 16, 256) 1024 ['conv2d_14[0][0]']
ormalization)
leaky_re_lu_14 (LeakyReLU) (None, 16, 16, 256) 0 ['batch_normalization_14[0][0]']
conv2d_15 (Conv2D) (None, 16, 16, 256) 589824 ['leaky_re_lu_14[0][0]']
add_5 (Add) (None, 16, 16, 256) 0 ['conv2d_15[0][0]',
'add_4[0][0]']
batch_normalization_15 (BatchN (None, 16, 16, 256) 1024 ['add_5[0][0]']
ormalization)
leaky_re_lu_15 (LeakyReLU) (None, 16, 16, 256) 0 ['batch_normalization_15[0][0]']
conv2d_16 (Conv2D) (None, 16, 16, 512) 131072 ['leaky_re_lu_15[0][0]']
max_pooling2d_3 (MaxPooling2D) (None, 8, 8, 512) 0 ['conv2d_16[0][0]']
batch_normalization_16 (BatchN (None, 8, 8, 512) 2048 ['max_pooling2d_3[0][0]']
ormalization)
leaky_re_lu_16 (LeakyReLU) (None, 8, 8, 512) 0 ['batch_normalization_16[0][0]']
conv2d_17 (Conv2D) (None, 8, 8, 512) 2359296 ['leaky_re_lu_16[0][0]']
batch_normalization_17 (BatchN (None, 8, 8, 512) 2048 ['conv2d_17[0][0]']
ormalization)
leaky_re_lu_17 (LeakyReLU) (None, 8, 8, 512) 0 ['batch_normalization_17[0][0]']
conv2d_18 (Conv2D) (None, 8, 8, 512) 2359296 ['leaky_re_lu_17[0][0]']
add_6 (Add) (None, 8, 8, 512) 0 ['conv2d_18[0][0]',
'max_pooling2d_3[0][0]']
batch_normalization_18 (BatchN (None, 8, 8, 512) 2048 ['add_6[0][0]']
ormalization)
leaky_re_lu_18 (LeakyReLU) (None, 8, 8, 512) 0 ['batch_normalization_18[0][0]']
conv2d_19 (Conv2D) (None, 8, 8, 512) 2359296 ['leaky_re_lu_18[0][0]']
batch_normalization_19 (BatchN (None, 8, 8, 512) 2048 ['conv2d_19[0][0]']
ormalization)
leaky_re_lu_19 (LeakyReLU) (None, 8, 8, 512) 0 ['batch_normalization_19[0][0]']
conv2d_20 (Conv2D) (None, 8, 8, 512) 2359296 ['leaky_re_lu_19[0][0]']
add_7 (Add) (None, 8, 8, 512) 0 ['conv2d_20[0][0]',
'add_6[0][0]']
batch_normalization_20 (BatchN (None, 8, 8, 512) 2048 ['add_7[0][0]']
ormalization)
leaky_re_lu_20 (LeakyReLU) (None, 8, 8, 512) 0 ['batch_normalization_20[0][0]']
conv2d_21 (Conv2D) (None, 8, 8, 1) 513 ['leaky_re_lu_20[0][0]']
up_sampling2d (UpSampling2D) (None, 128, 128, 1) 0 ['conv2d_21[0][0]']
==================================================================================================
Total params: 12,727,969
Trainable params: 12,718,305
Non-trainable params: 9,664
__________________________________________________________________________________________________
In [ ]:
import keras
!pip install pydicom
import pydicom
class generator(keras.utils.Sequence):
def __init__(self, folder, filenames, pneumonia_location_coordinates=None, batch_size=32, image_size=image_dimension, shuffle=True, augment=False, predict=False):
self.folder = folder
self.filenames = filenames
self.pneumonia_location_coordinates = pneumonia_location_coordinates
self.batch_size = batch_size
self.image_size = image_size
self.shuffle = shuffle
self.augment = augment
self.predict = predict
self.on_epoch_end()
def __load__(self, filename):
img_array = pydicom.dcmread(os.path.join(self.folder, filename), force=True).pixel_array
msk = np.zeros(img_array.shape)
filename = filename.split('.')[0]
if filename in self.pneumonia_location_coordinates:
for location in self.pneumonia_location_coordinates[filename]:
x, y, w, h = location
msk[y:y+h, x:x+w] = 1
img_array = resize(img_array, (self.image_size, self.image_size), mode='reflect')
msk = resize(msk, (self.image_size, self.image_size), mode='reflect')
if self.augment and random.random() > 0.5:
img_array = np.fliplr(img_array)
msk = np.fliplr(msk)
img_array = np.expand_dims(img_array, -1)
msk = np.expand_dims(msk, -1)
return img_array, msk
def __loadpredict__(self, filename):
img_array = pydicom.dcmread(os.path.join(self.folder, filename), force=True).pixel_array
img_array = resize(img_array, (self.image_size, self.image_size), mode='reflect')
img_array = np.expand_dims(img_array, -1)
return img_array
def __getitem__(self, index):
filenames = self.filenames[index*self.batch_size:(index+1)*self.batch_size]
if self.predict:
imgs = [self.__loadpredict__(filename) for filename in filenames]
imgs = np.array(imgs)
return imgs, filenames
else:
items = [self.__load__(filename) for filename in filenames]
imgs, msks = zip(*items)
imgs = np.array(imgs)
msks = np.array(msks)
return imgs, msks
def on_epoch_end(self):
if self.shuffle:
random.shuffle(self.filenames)
def __len__(self):
if self.predict:
return int(np.ceil(len(self.filenames) / self.batch_size))
else:
return int(len(self.filenames) / self.batch_size)
train_gen = generator('/content/stage_2_train_images/', train_data_filenames, pneumonia_location_coordinates, batch_size=32, image_size=image_dimension, shuffle=True, augment=True, predict=False)
valid_gen = generator('/content/stage_2_train_images/', validation_data_filenames, pneumonia_location_coordinates, batch_size=32, image_size=image_dimension, shuffle=False, predict=False)
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/ Requirement already satisfied: pydicom in /usr/local/lib/python3.8/dist-packages (2.3.1)
In [ ]:
import tensorflow as tf
learning_rate = tf.keras.callbacks.LearningRateScheduler(cosine_annealing)
early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0.005, patience=4, restore_best_weights=True, verbose=1, mode='auto')
history = model.fit(train_gen, validation_data=valid_gen, callbacks=[learning_rate, early_stopping], epochs=5, workers=4, use_multiprocessing=True)
Epoch 1/5 583/583 [==============================] - 2928s 5s/step - loss: 0.4863 - accuracy: 0.9548 - mean_iou: 0.6715 - val_loss: 0.4576 - val_accuracy: 0.9584 - val_mean_iou: 0.7056 - lr: 0.0010 Epoch 2/5 583/583 [==============================] - 2918s 5s/step - loss: 0.4544 - accuracy: 0.9594 - mean_iou: 0.7064 - val_loss: 0.4637 - val_accuracy: 0.9502 - val_mean_iou: 0.6483 - lr: 9.9606e-04 Epoch 3/5 583/583 [==============================] - 2930s 5s/step - loss: 0.4457 - accuracy: 0.9605 - mean_iou: 0.7163 - val_loss: 0.4506 - val_accuracy: 0.9541 - val_mean_iou: 0.6608 - lr: 9.8429e-04 Epoch 4/5 583/583 [==============================] - 2938s 5s/step - loss: 0.4360 - accuracy: 0.9617 - mean_iou: 0.7229 - val_loss: 0.4526 - val_accuracy: 0.9541 - val_mean_iou: 0.6564 - lr: 9.6489e-04 Epoch 5/5 583/583 [==============================] - 2947s 5s/step - loss: 0.4318 - accuracy: 0.9621 - mean_iou: 0.7281 - val_loss: 0.4333 - val_accuracy: 0.9604 - val_mean_iou: 0.7301 - lr: 9.3815e-04
In [ ]:
# convert the model to a JSON string
model_json = model.to_json()
# save the JSON string to a file
with open("/content/drive/MyDrive/model.json", "w") as json_file:
json_file.write(model_json)
model.save_weights("/content/drive/MyDrive/model.h5")
In [ ]:
from keras.models import model_from_json
# load json and create model
json_file = open('/content/drive/MyDrive/model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
# load weights into new model
loaded_model.load_weights("/content/drive/MyDrive/model.h5")
Step 4: Pickle the model for future prediction [ 3 Points]
In [ ]:
picklefile = open('drive/My Drive/cv capstone/final_object_detection', 'wb')
pickle.dump(model, picklefile)
picklefile.close()
WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 22). These functions will not be directly callable after loading.
In [ ]:
plt.figure(figsize=(25,6))
plt.subplot(131)
plt.plot(history.epoch, history.history["loss"], label="Train loss")
plt.plot(history.epoch, history.history["val_loss"], label="Valid loss")
plt.legend()
plt.subplot(132)
plt.plot(history.epoch, history.history["accuracy"], label="Train accuracy")
plt.plot(history.epoch, history.history["val_accuracy"], label="Valid accuracy")
plt.legend()
plt.subplot(133)
plt.plot(history.epoch, history.history["mean_iou"], label="Train iou")
plt.plot(history.epoch, history.history["val_mean_iou"], label="Valid iou")
plt.legend()
plt.show()
In [ ]:
from matplotlib import pyplot as plt
import matplotlib.patches as patches
# Red is predicted mask, Blue is actual Mask
for images, masks in valid_gen:
predictions = model.predict(images)
fig, axarr = plt.subplots(4, 8, figsize=(20,15))
axidx = 0
axarr = axarr.ravel()
for image, mask, pred in zip(images, masks, predictions):
axarr[axidx].imshow(image[:, :, 0])
component = mask[:, :, 0] > 0.5
component = measure.label(component)
for region in measure.regionprops(component):
y1, x1, y2, x2 = region.bbox
height = y2 - y1
width = x2 - x1
axarr[axidx].add_patch(patches.Rectangle((x1,y1),width,height,linewidth=2,edgecolor='b',facecolor='none'))
component = pred[:, :, 0] > 0.5
component = measure.label(component)
for region in measure.regionprops(component):
y1, x1, y2, x2 = region.bbox
height = y2 - y1
width = x2 - x1
axarr[axidx].add_patch(patches.Rectangle((x1,y1),width,height,linewidth=2,edgecolor='r',facecolor='none'))
axidx += 1
plt.show()
break
1/1 [==============================] - 0s 369ms/step
In [ ]:
# load and shuffle filenames
folder = '/content/stage_2_test_images'
test_filenames = os.listdir(folder)
print('n test samples:', len(test_filenames))
# create test generator with predict flag set to True
test_gen = generator(folder, test_filenames, None, batch_size=25, image_size=128, shuffle=False, predict=True)
# create submission dictionary
submission_dict = {}
# loop through testset
for imgs, filenames in test_gen:
# predict batch of images
preds = model.predict(imgs)
# loop through batch
for pred, filename in zip(preds, filenames):
# resize predicted mask
pred = resize(pred, (1024, 1024), mode='reflect')
# threshold predicted mask
comp = pred[:, :, 0] > 0.5
# apply connected components
comp = measure.label(comp)
# apply bounding boxes
predictionString = ''
for region in measure.regionprops(comp):
# retrieve x, y, height and width
y, x, y2, x2 = region.bbox
height = y2 - y
width = x2 - x
# proxy for confidence score
conf = np.mean(pred[y:y+height, x:x+width])
# add to predictionString
predictionString += str(conf) + ' ' + str(x) + ' ' + str(y) + ' ' + str(width) + ' ' + str(height) + ' '
# add filename and predictionString to dictionary
filename = filename.split('.')[0]
submission_dict[filename] = predictionString
# stop if we've got them all
if len(submission_dict) >= len(test_filenames):
break
# save dictionary as csv file
sub = pd.DataFrame.from_dict(submission_dict,orient='index')
sub.index.names = ['patientId']
sub.columns = ['PredictionString']
sub.to_csv('/content/drive/MyDrive/submission.csv')
n test samples: 3000 1/1 [==============================] - 1s 971ms/step 1/1 [==============================] - 0s 22ms/step 1/1 [==============================] - 0s 22ms/step 1/1 [==============================] - 0s 27ms/step 1/1 [==============================] - 0s 21ms/step 1/1 [==============================] - 0s 22ms/step 1/1 [==============================] - 0s 23ms/step 1/1 [==============================] - 0s 25ms/step 1/1 [==============================] - 0s 22ms/step 1/1 [==============================] - 0s 22ms/step 1/1 [==============================] - 0s 23ms/step 1/1 [==============================] - 0s 22ms/step 1/1 [==============================] - 0s 27ms/step 1/1 [==============================] - 0s 22ms/step 1/1 [==============================] - 0s 27ms/step 1/1 [==============================] - 0s 27ms/step 1/1 [==============================] - 0s 27ms/step 1/1 [==============================] - 0s 23ms/step 1/1 [==============================] - 0s 23ms/step 1/1 [==============================] - 0s 26ms/step 1/1 [==============================] - 0s 22ms/step 1/1 [==============================] - 0s 28ms/step 1/1 [==============================] - 0s 23ms/step 1/1 [==============================] - 0s 25ms/step 1/1 [==============================] - 0s 22ms/step 1/1 [==============================] - 0s 22ms/step 1/1 [==============================] - 0s 25ms/step 1/1 [==============================] - 0s 23ms/step 1/1 [==============================] - 0s 23ms/step 1/1 [==============================] - 0s 23ms/step 1/1 [==============================] - 0s 23ms/step 1/1 [==============================] - 0s 23ms/step 1/1 [==============================] - 0s 27ms/step 1/1 [==============================] - 0s 28ms/step 1/1 [==============================] - 0s 28ms/step 1/1 [==============================] - 0s 22ms/step 1/1 [==============================] - 0s 22ms/step 1/1 [==============================] - 0s 23ms/step 1/1 [==============================] - 0s 26ms/step 1/1 [==============================] - 0s 26ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 24ms/step 1/1 [==============================] - 0s 27ms/step 1/1 [==============================] - 0s 25ms/step 1/1 [==============================] - 0s 25ms/step 1/1 [==============================] - 0s 25ms/step 1/1 [==============================] - 0s 28ms/step 1/1 [==============================] - 0s 38ms/step 1/1 [==============================] - 0s 28ms/step 1/1 [==============================] - 0s 23ms/step 1/1 [==============================] - 0s 22ms/step 1/1 [==============================] - 0s 23ms/step 1/1 [==============================] - 0s 23ms/step 1/1 [==============================] - 0s 26ms/step 1/1 [==============================] - 0s 23ms/step 1/1 [==============================] - 0s 24ms/step 1/1 [==============================] - 0s 23ms/step 1/1 [==============================] - 0s 27ms/step 1/1 [==============================] - 0s 27ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 30ms/step 1/1 [==============================] - 0s 25ms/step 1/1 [==============================] - 0s 22ms/step 1/1 [==============================] - 0s 26ms/step 1/1 [==============================] - 0s 22ms/step 1/1 [==============================] - 0s 22ms/step 1/1 [==============================] - 0s 28ms/step 1/1 [==============================] - 0s 23ms/step 1/1 [==============================] - 0s 25ms/step 1/1 [==============================] - 0s 25ms/step 1/1 [==============================] - 0s 27ms/step 1/1 [==============================] - 0s 31ms/step 1/1 [==============================] - 0s 25ms/step 1/1 [==============================] - 0s 22ms/step 1/1 [==============================] - 0s 26ms/step 1/1 [==============================] - 0s 28ms/step 1/1 [==============================] - 0s 23ms/step 1/1 [==============================] - 0s 25ms/step 1/1 [==============================] - 0s 22ms/step 1/1 [==============================] - 0s 23ms/step 1/1 [==============================] - 0s 22ms/step 1/1 [==============================] - 0s 23ms/step 1/1 [==============================] - 0s 24ms/step 1/1 [==============================] - 0s 25ms/step 1/1 [==============================] - 0s 25ms/step 1/1 [==============================] - 0s 30ms/step 1/1 [==============================] - 0s 27ms/step 1/1 [==============================] - 0s 23ms/step 1/1 [==============================] - 0s 36ms/step 1/1 [==============================] - 0s 24ms/step 1/1 [==============================] - 0s 23ms/step 1/1 [==============================] - 0s 27ms/step 1/1 [==============================] - 0s 24ms/step 1/1 [==============================] - 0s 23ms/step 1/1 [==============================] - 0s 23ms/step 1/1 [==============================] - 0s 27ms/step 1/1 [==============================] - 0s 22ms/step 1/1 [==============================] - 0s 27ms/step 1/1 [==============================] - 0s 26ms/step 1/1 [==============================] - 0s 23ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 25ms/step 1/1 [==============================] - 0s 26ms/step 1/1 [==============================] - 0s 38ms/step 1/1 [==============================] - 0s 27ms/step 1/1 [==============================] - 0s 28ms/step 1/1 [==============================] - 0s 23ms/step 1/1 [==============================] - 0s 26ms/step 1/1 [==============================] - 0s 31ms/step 1/1 [==============================] - 0s 27ms/step 1/1 [==============================] - 0s 27ms/step 1/1 [==============================] - 0s 26ms/step 1/1 [==============================] - 0s 23ms/step 1/1 [==============================] - 0s 22ms/step 1/1 [==============================] - 0s 27ms/step 1/1 [==============================] - 0s 22ms/step 1/1 [==============================] - 0s 28ms/step 1/1 [==============================] - 0s 40ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 26ms/step
There was a considerable IOU for the images where prediction was correct.
In [ ]:
subm=pd.read_csv("/content/drive/MyDrive/submission.csv")
In [ ]:
subm.sample(10)
Out[ ]:
| patientId | PredictionString | |
|---|---|---|
| 2680 | 116dd2ea-16d5-4765-b800-cb77b72dbcf5 | NaN |
| 872 | 22d4c0ce-db48-4a4d-9653-a9e362c1f812 | NaN |
| 324 | 1d832224-bbc0-46f7-af50-27546292dcd7 | NaN |
| 218 | 1ad5385c-07a9-4f5d-aaad-83774ec96692 | NaN |
| 2087 | 148243ba-ad33-454e-b7e9-a63f66affc8c | 0.8770801 637 381 259 514 0.8279368 128 383 38... |
| 1081 | 2c75136a-992f-477a-8a51-ee23fed87917 | NaN |
| 270 | 1dd44ba2-d479-4f24-a7b0-e1e934aaea65 | 0.9145001 256 383 256 385 0.5633788 643 387 12... |
| 1770 | 009ac78b-6b5c-45ef-b73b-855116887af4 | NaN |
| 1889 | 02bc74a8-2ea2-4d73-a7b4-d2ae9c1ddfd7 | NaN |
| 1557 | 136feda3-a969-4a76-ac47-9e72a43a1af7 | NaN |
Thus based on our evaluation metric, the scores of the models we tried are as below for Target = 1 on test data:
| Model | Precision% | Recall% | F1-Score% | Accuracy% |
|---|---|---|---|---|
| Basic CNN model | 62 | 54 | 52 | 69 |
| Fine tuned Basic CNN model | 56 | 60 | 58 | 72 |
| DenseNet with Validation data | 72 | 60 | 66 | 80 |
| DenseNet | 73 | 79 | 76 | 84 |
| VggNet16 | 57 | 81 | 67 | 75 |
| VggNet19 | 64 | 54 | 59 | 76 |
| ResNet | 79 | 56 | 66 | 82 |
| InceptionNet | 64 | 72 | 68 | 78 |
DenseNet with Adam optimizer trained for 20 epochs with data split as train and test in ratio 70:30 showed better result than all other models.
Boundin box Prediction model has validation accuracy of 96% and validation iou of 73%.
In [ ]: